Diagnostics

31 pages, 18877 KB

Open AccessReview

Imaging Evaluation for Jaw Deformities: Diagnostic Workup and Pre-Treatment Imaging Checklist for Orthognathic Surgery

by Hiroki Tsurushima, Masafumi Oda, Kaori Kometani-Gunjikake, Tomohiko Shirakawa, Shinobu Matsumoto-Takeda, Nao Wakasugi-Sato, Shun Nishimura, Kazuya Haraguchi, Susumu Nishina, Tatsuo Kawamoto, Manabu Habu, Izumi Yoshioka, Toshiaki Arimatsu and Yasuhiro Morimoto

Diagnostics 2026, 16(2), 367; https://doi.org/10.3390/diagnostics16020367 - 22 Jan 2026

Viewed by 877

Abstract

In addition to standardized lateral cephalometric radiographs, comprehensive assessment using dental cone-beam computed tomography (CBCT) and CT has become commonplace in the diagnosis and treatment of jaw deformities. Simulation based on cephalometric and CT data is particularly useful in the management of jaw [...] Read more.

In addition to standardized lateral cephalometric radiographs, comprehensive assessment using dental cone-beam computed tomography (CBCT) and CT has become commonplace in the diagnosis and treatment of jaw deformities. Simulation based on cephalometric and CT data is particularly useful in the management of jaw deformities, both for evaluation and prognostic prediction. As such imaging examinations cover a wide anatomical region, it is not uncommon for various incidental pathologies to be discovered. This review emphasizes the necessity of evaluating the entire imaged area in addition to the chief complaint. Furthermore, it outlines the essential anatomical structures that should be assessed during diagnostic imaging performed prior to representative surgical procedures for jaw deformities (e.g., sagittal split ramus osteotomy and Le Fort I osteotomy). This review paper is descriptive in nature, incorporating our facility’s empirical aspects, and presents representative cases in a narrative format; it is not a systematic review. In other word, as the evidence-based literature does not cover all aspects of pretreatment evaluation, these criteria are based on the past experience of the authors. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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14 pages, 287 KB

Open AccessReview

Three-Dimensional Reconstruction and Navigation Systems in Endoscopic Ultrasound Procedures: A Comprehensive Review

by Eyad Gadour, Bogdan Miutescu, Bodour Raheem, Abed Al-Lehibi, Abdulrahman Alfadda, Ana Maria Ghiuchici and Antonio Facciorusso

Diagnostics 2026, 16(2), 366; https://doi.org/10.3390/diagnostics16020366 - 22 Jan 2026

Viewed by 649

Abstract

Three-dimensional (3D) reconstruction of ultrasound (US) images represents a novel advancement that has been extensively explored over the past three decades. This technique enables endoscopists to perform more detailed and enhanced visualizations of anatomical structures, which is not feasible using traditional ultrasound methods. [...] Read more.

Three-dimensional (3D) reconstruction of ultrasound (US) images represents a novel advancement that has been extensively explored over the past three decades. This technique enables endoscopists to perform more detailed and enhanced visualizations of anatomical structures, which is not feasible using traditional ultrasound methods. The reconstructed images also facilitate navigation during endoscopy-guided procedures, such as fine-needle aspiration. Furthermore, augmented reality (AR) algorithms can overlay the reconstructed images with real-time anatomical images, thereby enhancing clinician performance during these procedures. Current evidence suggests that 3D ultrasound reconstruction has already been widely implemented in various clinical imaging studies. However, its application for generating procedural guidance and augmented reality overlays remains in the early research stages and has not yet achieved widespread adoption. Existing pre-clinical evidence suggests that 3D reconstruction has significant potential to enhance clinician performance in various ultrasound-guided procedures. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

22 pages, 829 KB

Open AccessReview

Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review

by Bianka Andrzejczak, Aleksandra Diedul, Anna Szczepankiewicz, Piotr Trojanowski, Antoni Skrzypczak, Anna Bączkiewicz, Hanna Szymańska, Marzena Liliana Wyganowska and Zuzanna Ślebioda

Diagnostics 2026, 16(2), 365; https://doi.org/10.3390/diagnostics16020365 - 22 Jan 2026

Viewed by 633

Abstract

Artificial Intelligence (AI) is a computer science that focuses on developing systems and machines capable of performing tasks that typically require human cognitive abilities. It has widespread applications in medical diagnostics. Its use has led to rapid advancements in diagnostic methodology, enabling the [...] Read more.

Artificial Intelligence (AI) is a computer science that focuses on developing systems and machines capable of performing tasks that typically require human cognitive abilities. It has widespread applications in medical diagnostics. Its use has led to rapid advancements in diagnostic methodology, enabling the analysis of large datasets. The major applications of AI in medical diagnostics include personalized treatment based on patient genetics, preventive measures, and medical image analysis. AI is employed to analyse genomic data and biomarkers, aiding in the precise tailoring of therapies to individual patient needs. It could also be employed in modern dentistry in the near future, helping to achieve higher efficiency and accuracy in diagnosis and treatment planning. AI may be utilized in screening for oral mucosa lesions and to discriminate between oral potentially malignant disorders and cancers from benign lesions. The potential advantages of AI include high speed and accuracy in the diagnostic process, as well as relatively low costs. The aim of this review was to present the potential applications of AI methods in the diagnosis of selected mucocutaneous diseases. A literature review focuses on oral lichen planus, recurrent aphthous stomatitis, and oral and laryngeal leukoplakia. Full article

(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)

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20 pages, 592 KB

Open AccessReview

Detection of Feigned Impairment of the Shoulder Due to External Incentives: A Comprehensive Review

by Nahum Rosenberg

Diagnostics 2026, 16(2), 364; https://doi.org/10.3390/diagnostics16020364 - 22 Jan 2026

Viewed by 684

Abstract

Background: Feigned restriction of shoulder joint movement for secondary gain is clinically relevant and may misdirect care, distort disability determinations, and inflate system costs. Distinguishing feigning from structural pathology and from functional or psychosocial presentations is difficult because pain is subjective, performance varies, [...] Read more.

Background: Feigned restriction of shoulder joint movement for secondary gain is clinically relevant and may misdirect care, distort disability determinations, and inflate system costs. Distinguishing feigning from structural pathology and from functional or psychosocial presentations is difficult because pain is subjective, performance varies, and no single sign or test is definitive. This comprehensive review hypothesizes that the systematic integration of clinical examination, objective biomechanical and neurophysiological testing, and emerging technologies can substantially improve detection accuracy and provide defensible medicolegal documentation. Methods: PubMed and reference lists were searched within a prespecified time frame (primarily 2015–2025, with foundational earlier works included when conceptually essential) using terms related to shoulder movement restriction, malingering/feigning, symptom validity, effort testing, functional assessment, and secondary gain. Evidence was synthesized narratively, emphasizing objective or semi-objective quantification of motion and effort (goniometry, dynamometry, electrodiagnostics, kinematic sensing, and imaging). Results: Detection is best approached as a stepwise, multidimensional evaluation. First-line clinical assessment focuses on reproducible incongruence: non-anatomic patterns, internal inconsistencies, distraction-related improvement, and mismatch between claimed disability and observed function. Repeated examinations and documentation strengthen inference. Instrumented strength testing improves quantification beyond manual testing but remains effort-dependent; repeat-trial variability and atypical agonist–antagonist co-activation can indicate submaximal performance without proving intent. Imaging primarily tests plausibility by confirming lesions or highlighting discordance between claimed limitation and minimal pathology, while recognizing that normal imaging does not exclude pain. Diagnostic anesthetic injections and electrodiagnostics can clarify pain-mediated restriction or exclude neuropathic weakness but require cautious interpretation. Motion capture and inertial sensors can document compensatory strategies and context-dependent normalization, yet validated standalone thresholds are limited. Conclusions: Feigned shoulder impairment cannot be confirmed by any single test. The desirable strategy combines structured assessment of inconsistencies with objective biomechanical and neurophysiologic measurements, interpreted within the whole clinical context and rigorously documented; however, prospective validation is still needed before routine implementation. Full article

(This article belongs to the Special Issue Orthopedics and the Musculoskeletal System: Diagnosis, Prognosis, and Mechanisms)

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30 pages, 3878 KB

Open AccessArticle

MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder

by Rabeah AlAqel, Muhammad Hussain and Saad Al-Ahmadi

Diagnostics 2026, 16(2), 363; https://doi.org/10.3390/diagnostics16020363 - 22 Jan 2026

Viewed by 616

Abstract

Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing [...] Read more.

Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing machine learning (ML) and deep learning (DL) methods. Although DL methods are promising for MDD detection, they face limitations, including high model complexity, overfitting due to subject-specific noise, excessive channel requirements, and limited interpretability. Methods: To address these challenges, we propose MS-MDDNet, a new lightweight CNN model specifically designed for EEG-based MDD detection, along with an ensemble-like method built on it. The architecture of MS-MDDNet incorporates spatial, temporal, and depth-wise separable convolutions, along with average pooling, to enhance discriminative feature extraction while maintaining computational efficiency with a small number of learnable parameters. Results: The method was evaluated using 10-fold Cross-Subjects Cross-Validation (CS-CV), which mitigates the risks of overfitting associated with subject-specific noise, thereby contributing to generalization robustness. Across three public datasets, the proposed method achieved performance comparable to state-of-the-art approaches while maintaining lower computational complexity. It achieved a 9% improvement on the MODMA dataset, with an accuracy of 99.33%, whereas on MUMTAZ and PRED + CT it achieved accuracies of 98.59% and 96.61%, respectively. Conclusions: The predictions of the proposed method are interpretable, with interpretability achieved through correlation analysis between gamma energy and learned features. This makes it a valuable tool for assisting clinicians and individuals in diagnosing MDD with confidence, thereby enhancing transparency in decision-making and promoting clinical credibility. Full article

(This article belongs to the Special Issue EEG Analysis in Diagnostics, 2nd Edition)

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24 pages, 2692 KB

Open AccessArticle

Domain Shift in Breast DCE-MRI Tumor Segmentation: A Balanced LoCoCV Study on the MAMA-MIA Dataset

by Munid Alanazi and Bader Alsharif

Diagnostics 2026, 16(2), 362; https://doi.org/10.3390/diagnostics16020362 - 22 Jan 2026

Viewed by 577

Abstract

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition [...] Read more.

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition protocols, and patient populations differ from those in the training data. This study investigates how such center-related domain shift affects automated breast DCE-MRI tumor segmentation on the multi-center MAMA-MIA dataset. Methods: We trained a standard 3D U-Net for primary tumor segmentation under two evaluation settings. First, we constructed a random patient-wise split that mixes cases from the three main MAMA-MIA center groups (ISPY2, DUKE, NACT) and used this as an in-distribution reference. Second, we designed a balanced leave-one-center-out cross-validation (LoCoCV) protocol in which each center is held out in turn, while training, validation, and test sets are matched in size across folds. Performance was assessed using the Dice similarity coefficient, 95th percentile Hausdorff distance (HD95), sensitivity, specificity, and related overlap measures. Results: On the mixed-center random split, the best three-channel model achieved a mean Dice of about 0.68 and a mean HD95 of about 19.7 mm on the held-out test set, indicating good volumetric overlap and boundary accuracy when training and test distributions match. Under balanced LoCoCV, the one-channel model reached a mean Dice of about 0.45 and a mean HD95 of about 41 mm on unseen centers, with similar averages for the three-channel variant. Compared with the random split baseline, Dice and sensitivity decreased, while HD95 nearly doubled, showing that boundary errors become larger and segmentations less reliable when the model is applied to new centers. Conclusions: A model that performs well on mixed-center random splits can still suffer a substantial loss of accuracy on completely unseen institutions. The balanced LoCoCV design makes this out-of-distribution penalty visible by separating center-related effects from sample size effects. These findings highlight the need for robust multi-center training strategies and explicit cross-center validation before deploying breast DCE-MRI segmentation models in clinical practice. Full article

(This article belongs to the Special Issue AI in Radiology and Nuclear Medicine: Challenges and Opportunities)

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18 pages, 1994 KB

Open AccessArticle

Experimental Lung Ultrasound Scoring in a Murine Model of Aspiration Pneumonia: Challenges and Diagnostic Perspectives

by Ching-Wei Chuang, Wen-Yi Lai, Kuo-Wei Chang, Chao-Yuan Chang, Shang-Ru Yeoh and Chun-Jen Huang

Diagnostics 2026, 16(2), 361; https://doi.org/10.3390/diagnostics16020361 - 22 Jan 2026

Viewed by 602

Abstract

Background: Aspiration pneumonia (AP) remains a major cause of morbidity and mortality, yet non-invasive tools for monitoring lung injury in preclinical models are limited. Lung ultrasound (LUS) is widely used clinically, but existing murine scoring systems lack anatomical resolution and have not been [...] Read more.

Background: Aspiration pneumonia (AP) remains a major cause of morbidity and mortality, yet non-invasive tools for monitoring lung injury in preclinical models are limited. Lung ultrasound (LUS) is widely used clinically, but existing murine scoring systems lack anatomical resolution and have not been validated for aspiration-related injury. Methods: We developed the Modified Lung Edema Ultrasound Score (MLEUS), a region-structured adaptation of the Mouse Lung Ultrasound Score (MoLUS), designed to accommodate the heterogeneous and gravity-dependent injury patterns characteristic of murine AP. Male C57BL/6 mice were assigned to sham, 6 h, 24 h, or 48 h groups. Regional LUS findings were compared with histological injury scores and wet-to-dry (W/D) ratios. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Results: Global LUS–histology correlation was weak (ρ = 0.33, p = 0.114). In contrast, regional performance varied markedly. The right upper (RU) zone showed the strongest correspondence with histological injury (r = 0.55, p = 0.005), whereas right and left diaphragmatic regions demonstrated minimal association. LUS abnormalities were detectable as early as 6 h, preceding clear histological progression. Inter-rater reliability was good (ICC = 0.87). Conclusions: MLEUS provides a reproducible, region-specific framework for evaluating aspiration-induced lung injury in mice. Although global correlations with histology were limited, region-dependent analysis identified that the RU zone as a reliable acoustic window for concurrent injury assessment. Early ultrasound changes highlight the sensitivity of LUS to dynamic aeration and interstitial alterations rather than cumulative tissue damage. These findings support the use of LUS as a complementary, non-invasive physiological monitoring tool in small-animal respiratory research and clarify its methodological scope relative to existing scoring frameworks. Full article

(This article belongs to the Special Issue Future Challenges for Lung and Liver Ultrasound)

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15 pages, 563 KB

Open AccessReview

Liquid Biopsy-Based Biomolecular Alterations for the Diagnosis of Triple-Negative Breast Cancer in Adults: A Scoping Review

by Orieta Navarrete-Fernández, Eddy Mora, Josue Rivadeneira, Víctor Herrera and Ángela L. Riffo-Campos

Diagnostics 2026, 16(2), 360; https://doi.org/10.3390/diagnostics16020360 - 22 Jan 2026

Cited by 1 | Viewed by 537

Abstract

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype, with limited diagnostic options and no targeted early detection tools. Liquid biopsy represents a minimally invasive approach for detecting tumor-derived molecular alterations in body fluids. This scoping review aimed to comprehensively synthesize all liquid [...] Read more.

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype, with limited diagnostic options and no targeted early detection tools. Liquid biopsy represents a minimally invasive approach for detecting tumor-derived molecular alterations in body fluids. This scoping review aimed to comprehensively synthesize all liquid biopsy-derived molecular biomarkers evaluated for the diagnosis of TNBC in adults. Methods: This review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Systematic searches of PubMed, Scopus, Embase, and Web of Science identified primary human studies evaluating circulating molecular biomarkers for TNBC diagnosis. Non-TNBC, non-human, hereditary, treatment-response, and nonmolecular studies were excluded. Data on study design, patient characteristics, biospecimen type, analytical platforms, biomarker class, and diagnostic performance were extracted and synthesized descriptively by biomolecule class. Results: Thirty-two studies met the inclusion criteria, comprising 15 protein-based, 12 RNA-based, and 6 DNA-based studies (one reporting both protein and RNA). In total, 1532 TNBC cases and 3137 participants in the comparator group were analyzed. Protein biomarkers were the most frequently studied, although only APOA4 appeared in more than one study, with conflicting results. RNA-based biomarkers identified promising candidates, particularly miR-21, but validation cohorts were scarce. DNA methylation markers showed promising diagnostic accuracy yet lacked replication. Most studies were small retrospective case–control designs with heterogeneous comparators and inconsistent diagnostic reporting. Conclusions: Evidence for liquid biopsy-derived biomarkers in TNBC remains limited, heterogeneous, and insufficiently validated. No biomarker currently shows reproducibility suitable for clinical implementation. Robust, prospective, and standardized studies are needed to advance liquid biopsy-based diagnostics in TNBC. Full article

(This article belongs to the Special Issue Utilization of Liquid Biopsy in Cancer Diagnosis and Management 2025)

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27 pages, 10518 KB

Open AccessArticle

DL-PCMNet: Distributed Learning Enabled Parallel Convolutional Memory Network for Skin Cancer Classification with Dermatoscopic Images

by Afnan M. Alhassan and Nouf I. Altmami

Diagnostics 2026, 16(2), 359; https://doi.org/10.3390/diagnostics16020359 - 22 Jan 2026

Viewed by 278

Abstract

Background/Objectives: Globally, one of the most dreadful and rapidly spreading illnesses is skin cancer, and it is acknowledged as a lethal form of cancer due to the abnormal growth of skin cells. Mostly, classifying and diagnosing the types of skin lesions is [...] Read more.

Background/Objectives: Globally, one of the most dreadful and rapidly spreading illnesses is skin cancer, and it is acknowledged as a lethal form of cancer due to the abnormal growth of skin cells. Mostly, classifying and diagnosing the types of skin lesions is complex, and recognizing tumors from dermoscopic images remains challenging. The existing methods have limitations like insufficient datasets, computational complexity, class imbalance issues, and poor classification performance. Methods: This research presents a method named the Distributed Learning enabled Parallel Convolutional Memory Network (DL-PCMNet) model to effectively classify skin cancer by overcoming the existing limitations. Hence, the proposed DL-PCMNet model utilizes a distributed learning framework to provide greater flexibility during the learning process, and it increases the reliability of the model. Moreover, the model integrates the Convolutional Neural Network (CNN) and Long Short-Term Memory model (LSTM) in a parallel distribution, which enhances robustness and accuracy by capturing the information of long-term dependencies. Furthermore, the utilization of advanced preprocessing and feature extraction techniques increases the accuracy of classification. Results: The evaluation results exhibit an accuracy of 97.28%, precision of 97.30%, sensitivity of 97.17%, and specificity of 97.72% at 90% of training by using the ISIC 2019 skin lesion dataset, respectively. Conclusions: Specifically, the proposed DL-PCMNet model achieved efficient and accurate skin cancer classification compared with other existing models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dermatology)

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16 pages, 9958 KB

Open AccessReview

The Role of Imaging Techniques in the Evaluation of Extraglandular Manifestations in Patients with Sjögren’s Syndrome

by Marcela Iojiban, Bogdan-Ioan Stanciu, Laura Damian, Lavinia Manuela Lenghel, Carolina Solomon and Monica Lupșor-Platon

Diagnostics 2026, 16(2), 358; https://doi.org/10.3390/diagnostics16020358 - 22 Jan 2026

Viewed by 551

Abstract

Sjögren’s syndrome is a chronic autoimmune disease marked by lymphocytic infiltration of the exocrine glands and the development of sicca symptoms, yet some patients also develop extraglandular involvement. Imaging has become relevant for describing these systemic features and supporting clinical assessment. This review [...] Read more.

Sjögren’s syndrome is a chronic autoimmune disease marked by lymphocytic infiltration of the exocrine glands and the development of sicca symptoms, yet some patients also develop extraglandular involvement. Imaging has become relevant for describing these systemic features and supporting clinical assessment. This review discusses the roles of ultrasonography, elastography, computed tomography, and magnetic resonance imaging in evaluating multisystem disease associated with Sjögren’s syndrome. Ultrasonography and elastography help assess muscular involvement by showing changes in echogenicity and stiffness that reflect inflammation and later tissue remodeling. In joints, ultrasound can detect synovitis, tenosynovitis, and early erosive changes, including abnormalities not yet evident on examination. Pulmonary disease, most often with interstitial lung involvement, is best evaluated with high-resolution computed tomography, which remains the most reliable imaging modality for distinguishing interstitial patterns. Magnetic resonance imaging is valuable in assessing neurological complications. It can reveal ischemic and demyelinating lesions, neuromyelitis optica spectrum features, or pseudotumoral appearances. Imaging is also essential for detecting lymphoproliferative complications, for which ultrasound and magnetic resonance imaging can reveal characteristic structural and diffusion-weighted imaging findings. When combined with clinical and laboratory information, these imaging methods improve early recognition of systemic involvement and support accurate monitoring of disease progression in Sjögren’s syndrome. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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23 pages, 6077 KB

Open AccessArticle

by Arvid Lundervold, Julie Billing, Birgitte Berentsen and Astri J. Lundervold

Diagnostics 2026, 16(2), 357; https://doi.org/10.3390/diagnostics16020357 - 22 Jan 2026

Viewed by 414

Abstract

Background: Irritable Bowel Syndrome (IBS) is a heterogeneous gastrointestinal disorder characterized by complex brain–gut interactions. Patient Similarity Networks (PSNs) offer a novel approach for exploring this heterogeneity and identifying clinically relevant patient subgroups. Methods: We analyzed data from 78 participants (49 IBS patients [...] Read more.

Background: Irritable Bowel Syndrome (IBS) is a heterogeneous gastrointestinal disorder characterized by complex brain–gut interactions. Patient Similarity Networks (PSNs) offer a novel approach for exploring this heterogeneity and identifying clinically relevant patient subgroups. Methods: We analyzed data from 78 participants (49 IBS patients and 29 healthy controls) with 36 brain morphometric measures (FreeSurfer v7.4.1) and 6 measures of cognitive functions (5 RBANS domain indices plus a Total Scale score). PSNs were constructed using multiple similarity measures (Euclidean, cosine, correlation-based) with Gaussian kernel transformation. We performed community detection (Louvain algorithm), centrality analyses, feature importance analysis, and correlations with symptom severity. Statistical validation included bootstrap confidence intervals and permutation testing. Results: The PSN comprised 78 nodes connected by 469 edges, with four communities detected. These communities did not significantly correspond to diagnostic groups (Adjusted Rand Index = 0.011, permutation

p = 0.212

), indicating IBS patients and healthy controls were intermixed. However, each community exhibited distinct neurobiological profiles: Community 1 (oldest, preserved cognition) showed elevated intracranial volume but reduced subcortical gray matter; Community 2 (youngest, most severe IBS symptoms) had elevated cortical volumes but reduced white matter; Community 3 (most balanced IBS/HC ratio, mildest IBS symptoms) showed the largest subcortical volumes; Community 4 (lowest cognitive performance across multiple domains) displayed the lowest RBANS scores alongside high IBS prevalence. Top network features included subcortical structures, corpus callosum, and cognitive indices (Language, Attention). Conclusions: PSN identifies brain–cognition communities that cut across diagnostic categories, with distinct feature profiles suggesting different hypothesis-generating neurobiological patterns within IBS that may inform personalized treatment strategies. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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16 pages, 1354 KB

Open AccessArticle

A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images

by Mohan Uttarwar, Jayant Khandare, P. M. Shivamurthy, Aditya Satpute, Mohit Panwar, Hrishita Kothavade, Aarthi Ramesh, Sandhya Iyer and Gowhar Shafi

Diagnostics 2026, 16(2), 356; https://doi.org/10.3390/diagnostics16020356 - 21 Jan 2026

Viewed by 672

Abstract

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in [...] Read more.

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in breast and ovarian cancer. However, identification of patients who will respond to Poly (ADP-ribose) polymerase (PARP) inhibitor therapy is challenging due to the lack of a unifying morphological phenotype. Current HRD testing via next-generation sequencing (NGS) is tissue-dependent, has high failure rates, misses relevant HRD genes, and involves longer turn-around times. Methods: To overcome these limitations, we developed a multimodal AI model, TRINITY, combining imaging, image-based transcriptome data, and clinico-molecular data, to examine whole-slide images (WSIs) obtained from hematoxylin and eosin (H&E)-stained samples to non-invasively predict HRD status. Results: The TRINITY model, tested on 316 TCGA breast and OV samples, presented a sensitivity of 0.77 and 0.91, NPV of 0.94 and 0.86, PPV of 0.63 and 0.58, specificity of 0.89 and 0.47, and AUC-ROC of 0.91 and 0.72, respectively. The model also yielded a similar outcome in a blind study of 74 samples, with a sensitivity of 81.2, NPV of 0.85, PPV of 0.77, specificity of 0.81, and high AUC-ROC value of 0.89, showing its promising preliminary evidence of predicting HRD status on external cohorts. Conclusions: These findings demonstrate TRINITY’s potential as a rapid, cost-effective, and tissue-sparing alternative to conventional NGS testing. While promising, further validation is needed to establish its generalizability across broader cancer types. Full article

(This article belongs to the Special Issue Recent Advances in Pathology 2026)

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15 pages, 1916 KB

Open AccessArticle

Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model

by Miriam Rinneburger, Heike Carolus, Andra-Iza Iuga, Mathilda Weisthoff, Simon Lennartz, Nils Große Hokamp, Liliana Lourenco Caldeira, Astha Jaiswal, David Maintz, Fabian Christopher Laqua, Bettina Baeßler, Tobias Klinder and Thorsten Persigehl

Diagnostics 2026, 16(2), 355; https://doi.org/10.3390/diagnostics16020355 - 21 Jan 2026

Viewed by 547

Abstract

Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis [...] Read more.

Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis or very large size. Here, we evaluate the performance of a previously developed generic cervical lymph node segmentation model in a cohort of patients with head and neck cancer. Methods: In our retrospective single-center, multi-vendor study, we included 125 patients with head and neck cancer with at least one untreated lymph node metastasis. On the respective cervical CT scan, an experienced radiologist segmented lymph nodes semi-automatically. All 3D segmentations were confirmed by a second reader. These manual segmentations were compared to segmentations generated by an AI model previously trained on a different dataset of varying cancers. Results: In cervical CT scans from 125 patients (61.9 years ± 10.6, 100 men), 3656 lymph nodes were segmented as ground-truth, including 544 clinical metastases. The AI achieved an average recall of 0.70 with 6.5 false positives per CT scan. The average global Dice accounts for 0.73 per scan, with an average Hausdorff distance of 0.88 mm. When analyzing the individual nodes, segmentation accuracy was similar for non-metastatic and metastatic lymph nodes, with a sensitivity of 0.89 and 0.85. Localization performance was lower for metastatic than for non-metastatic lymph nodes, with a recall of 0.65 and 0.74, respectively. Model performance was worse for enlarged nodes (short-axis diameter ≥ 15 mm), with a recall of 0.36 and a sensitivity of 0.67. Conclusions: The AI model for generic cervical lymph node segmentation shows good performance for smaller nodes (SAD ≤ 15 mm) with respect to localization and segmentation accuracy. However, for clearly enlarged and necrotic nodes, a retraining of the generic AI algorithm seems to be required for accurate cN staging. Full article

(This article belongs to the Special Issue Advances in Head and Neck and Oral Maxillofacial Radiology)

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15 pages, 5699 KB

Open AccessArticle

Short-Term Efficacy and Safety of Elobixibat for Chronic Constipation Assessed by Rectal Ultrasonography: A Retrospective Observational Study

by Momoko Tsuda, Tomoyuki Onodera, Kanako Konishi, Norishige Maiya, Mio Matsumoto, Kimitoshi Kubo, Sayaka Kudo, Yoshiyuki Hosoi and Mototsugu Kato

Diagnostics 2026, 16(2), 354; https://doi.org/10.3390/diagnostics16020354 - 21 Jan 2026

Viewed by 825

Abstract

Background/Objectives: Ultrasonography (US) is a non-invasive and repeatable examination for evaluating chronic constipation. However, few studies have explored treatment decisions based on rectal US findings. To date, the efficacy and safety of elobixibat have not been evaluated using rectal US classification in patients [...] Read more.

Background/Objectives: Ultrasonography (US) is a non-invasive and repeatable examination for evaluating chronic constipation. However, few studies have explored treatment decisions based on rectal US findings. To date, the efficacy and safety of elobixibat have not been evaluated using rectal US classification in patients with chronic constipation. This study aimed to evaluate the short-term efficacy and safety of elobixibat in patients with chronic constipation classified as “no fecal retention” by rectal US. Methods: We retrospectively analyzed 32 patients with chronic constipation who underwent rectal US and received elobixibat (10 mg/day) between May 2019 and December 2024. Rectal US findings classified patients into four groups: no fecal retention, fecal retention without hard stools, fecal retention with hard stools, and gas retention. The primary endpoint was the response rate of spontaneous bowel movements (SBMs) within 3 days after starting elobixibat in the “no fecal retention” group. Results: Among 18 patients in the “no fecal retention” group, 94.4% achieved SBMs within 3 days, indicating a favorable short-term response. Adverse events included abdominal distension and abdominal pain, each observed in one patient (3.1%). Conclusions: Elobixibat was effective and well tolerated in patients with chronic constipation classified by rectal US findings. Full article

(This article belongs to the Special Issue Diagnosis and Management of Colorectal Diseases, 2nd Edition)

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18 pages, 581 KB

Open AccessReview

AI-Enhanced POCUS in Emergency Care

by Monica Puticiu, Diana Cimpoesu, Florica Pop, Irina Ciumanghel, Luciana Teodora Rotaru, Bogdan Oprita, Mihai Alexandru Butoi, Vlad Ionut Belghiru, Raluca Mihaela Tat and Adela Golea

Diagnostics 2026, 16(2), 353; https://doi.org/10.3390/diagnostics16020353 - 21 Jan 2026

Cited by 2 | Viewed by 706

Abstract

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities [...] Read more.

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article

(This article belongs to the Special Issue Application of Ultrasound Imaging in Clinical Diagnosis)

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19 pages, 980 KB

Open AccessSystematic Review

Diagnostic Assessment of Periodontal and Dentoalveolar Complications Following Mini-Screw-Assisted Rapid Palatal Expansion in Adults and Late Adolescents: A Systematic Review

by Barbara Frenna, Raffaella Grimaldi, Salvatore Fiandaca, Renisa Basha, Monica Caprio, Giacomo Emanuele Maria Rizzo, Alessio Verdecchia and Enrico Spinas

Diagnostics 2026, 16(2), 352; https://doi.org/10.3390/diagnostics16020352 - 21 Jan 2026

Viewed by 614

Abstract

Objectives: This systematic review aimed to evaluate the effectiveness of currently available methods for the diagnosis and monitoring of skeletal, dental, and soft tissue changes, as well as the adequacy of follow-up protocols, in adolescents and adults treated with miniscrew-assisted rapid palatal [...] Read more.

Objectives: This systematic review aimed to evaluate the effectiveness of currently available methods for the diagnosis and monitoring of skeletal, dental, and soft tissue changes, as well as the adequacy of follow-up protocols, in adolescents and adults treated with miniscrew-assisted rapid palatal expansion (MARPE). Materials and Methods: This systematic review was conducted in accordance with the PRISMA guidelines. A comprehensive electronic literature search was performed across five databases (PubMed, Scopus, Embase, Cochrane, and Web of Science) to identify prospective and retrospective clinical studies evaluating dental, periodontal, and alveolar bone outcomes associated with MARPE in late adolescent and adult patients. Study selection, data extraction, and risk of bias assessment were independently performed by two reviewers. Risk of bias was assessed using the ROBINS-I tool for non-randomized studies and the RoB 2 tool for randomized studies. The certainty of the evidence was evaluated using the GRADE approach. Owing to substantial methodological heterogeneity and limited follow-up duration, a structured qualitative (narrative) synthesis of the results was performed. Results: A total of 20 studies were included in the systematic review. The reported adverse events primarily involved hard and soft tissues and were identified using cone-beam computed tomography (CBCT), clinical and periodontal examination, panoramic and cephalometric radiography, and digital dental casts. Dental effects, including dental tipping, were frequently reported across the included studies. Alveolar bone loss was reported in 11 studies, buccal alveolar bone dehiscence in 3 studies, and failure of palatal suture opening in 6 studies. In most of the included studies, follow-up was either not reported or limited. Conclusions: The MARPE technique appears to be potentially effective in achieving transverse maxillary expansion in late adolescent and adult patients. However, the included studies report possible adverse events affecting periodontal and alveolar bone tissues, such as alveolar bone thinning and gingival hypertrophy, the assessment of which requires an integrated diagnostic approach combining CBCT imaging with clinical and periodontal examination. Overall, the certainty of the available evidence was low to very low, mainly due to a high risk of bias, methodological heterogeneity, and limited or absent follow-up in most studies. Therefore, the results should be interpreted with caution. Well-designed prospective controlled studies with standardized protocols and long-term follow-up are needed to conclusively evaluate the safety and long-term clinical stability of the MARPE technique. Full article

(This article belongs to the Special Issue Diagnostic Approach and Innovations in the Different Dentistry Fields, 2nd Edition)

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13 pages, 1350 KB

Open AccessArticle

Autologous Osteochondral Transplantation in Large Osteochondral Defects—A Follow-Up of 40 Patients After Talus Re-Surfacing

by Alice Wittig-Draenert, Martin Breitwieser, Patrick Marko, Wolfgang Hitzl and Jürgen Bruns

Diagnostics 2026, 16(2), 351; https://doi.org/10.3390/diagnostics16020351 - 21 Jan 2026

Viewed by 349

Abstract

Background/Objectives: Large osteochondral lesions of the talus (OLT) pose a major challenge because their size and depth often exceed the indications for bone marrow stimulation, and durable biological repair remains difficult to achieve. However, evidence for autologous osteochondral transplantation (AOT) in extensive [...] Read more.

Background/Objectives: Large osteochondral lesions of the talus (OLT) pose a major challenge because their size and depth often exceed the indications for bone marrow stimulation, and durable biological repair remains difficult to achieve. However, evidence for autologous osteochondral transplantation (AOT) in extensive talar defects is still limited. Methods: In this retrospective cohort, 40 consecutive patients ≥ 14 years with ICRS grade III–IV lesions of the talar dome were treated with AOT at a tertiary referral center. One to three overlapping cylindrical osteochondral grafts (mean diameter 0.9 cm) were harvested from non-weight-bearing regions of the ipsilateral patellofemoral groove using a water-cooled diamond trephine system and implanted press-fit into the talar dome. Donor sites were refilled with autologous iliac crest bone cylinders and hydroxyapatite substitute. Pain (Numeric Rating Scale, NRS) and function (AOFAS Ankle–Hindfoot Score) were recorded preoperatively and at 3, 6, 9, and 12 months, and changes over time were analyzed using generalized estimating equations. Results: Mean defect size was 137.4 ± 31.9 mm², and 82.5% of lesions were ICRS grade III. NRS pain improved from 5.69 ± 2.52 preoperatively to 0.53 ± 0.98 at 12 months (p < 0.001). AOFAS score increased from 63.79 ± 2.55 to 97.36 ± 2.49 (p < 0.001). Age and graft location significantly influenced postoperative pain, whereas graft size and sex did not. No infections, graft failures, conversions to arthrodesis or arthroplasty, or clinically relevant donor-site symptoms occurred. Conclusions: Multi-plug AOT using a diamond trephine system provides substantial and durable pain relief and functional improvement in patients with large OLT, with low complication and donor-site morbidity rates. These findings support AOT as a joint-preserving option for extensive talar defects and justify further prospective, comparative studies with long-term follow-up. Full article

(This article belongs to the Special Issue Orthopedics and the Musculoskeletal System: Diagnosis, Prognosis, and Mechanisms)

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11 pages, 1157 KB

Open AccessArticle

Radiographic Evolution of Contralateral Asymptomatic Incomplete Atypical Femoral Fractures in Autoimmune Disease Patients

by Tomofumi Nishino, Kojiro Hyodo, Yukei Matsumoto, Yohei Yanagisawa, Koshiro Shimasaki, Ryunosuke Watanabe, Tomohiro Yoshizawa and Hajime Mishima

Diagnostics 2026, 16(2), 350; https://doi.org/10.3390/diagnostics16020350 - 21 Jan 2026

Viewed by 249

Abstract

Background/Objectives: Atypical femoral fracture (AFF) represents a diagnostic and therapeutic challenge, particularly in autoimmune disease patients receiving long-term bisphosphonate (BP) and glucocorticoid (GC) therapy. Although bilateral AFF is common, the radiographic evolution of asymptomatic incomplete lesions identified at the time of a complete [...] Read more.

Background/Objectives: Atypical femoral fracture (AFF) represents a diagnostic and therapeutic challenge, particularly in autoimmune disease patients receiving long-term bisphosphonate (BP) and glucocorticoid (GC) therapy. Although bilateral AFF is common, the radiographic evolution of asymptomatic incomplete lesions identified at the time of a complete fracture remains insufficiently defined. This study aimed to characterize the natural history and imaging biomarkers associated with progression in this biologically homogeneous high-risk population. Methods: Ten female autoimmune disease patients with complete AFF and asymptomatic incomplete contralateral lesions were retrospectively evaluated over a mean 59 months. Serial radiographs were assessed for cortical beaking, periosteal flaring, and transverse radiolucent lines. All patients discontinued BP therapy postoperatively; teriparatide was administered when tolerated. Results: Six lesions regressed, three remained stable, and one progressed—this progressing case being the only limb with a transverse radiolucent line at baseline. No patient developed symptoms or sustained a complete fracture on the contralateral side. Radiographic remodeling occurred independently of symptoms. BP discontinuation and, when tolerated, teriparatide appeared to contribute to lesion stabilization, although statistical significance was not achieved. Conclusions: In autoimmune patients with severe long-term BP and GC exposure, most asymptomatic incomplete AFF identified at the time of contralateral complete fracture remains stable or improves under conservative management. A transverse radiolucent line is the most decisive imaging biomarker predictive of progression and warrants intensified surveillance or consideration of prophylactic fixation. Larger cohorts are needed to refine risk stratification algorithms and optimize diagnostic and management strategies. Full article

(This article belongs to the Special Issue Advances in the Diagnosis and Management of Musculoskeletal Diseases and Bone Fracture Risk)

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17 pages, 989 KB

Open AccessSystematic Review

Neonatal Sepsis as Organ Dysfunction: Prognostic Accuracy and Clinical Utility of the nSOFA in the NICU—A Systematic Review

by Bogdan Cerbu, Marioara Boia, Manuela Pantea, Teodora Ignat, Mirabela Dima, Ileana Enatescu, Bogdan Rotea, Andra Rotea, Vlad David and Daniela Iacob

Diagnostics 2026, 16(2), 349; https://doi.org/10.3390/diagnostics16020349 - 21 Jan 2026

Viewed by 639

Abstract

Background and Objectives: Early recognition of life-threatening organ dysfunction is central to modern sepsis frameworks. We systematically reviewed the prognostic performance and clinical utility of the Neonatal Sequential Organ Failure Assessment (nSOFA) for mortality and major morbidity in NICU populations. The search identified [...] Read more.

Background and Objectives: Early recognition of life-threatening organ dysfunction is central to modern sepsis frameworks. We systematically reviewed the prognostic performance and clinical utility of the Neonatal Sequential Organ Failure Assessment (nSOFA) for mortality and major morbidity in NICU populations. The search identified 939 records across databases; after screening and full-text assessment, 16 studies met the inclusion criteria. Methods: Following PRISMA guidance, we searched major databases (2019–2025) for observational or interventional studies reporting discrimination or risk stratification using nSOFA in neonates. Populations included suspected/proven infection and condition-specific cohorts. Heterogeneity in timing, thresholds, and outcomes precluded meta-analysis. Results: A cumulative sample exceeding 25,000 neonates was identified across late- and early-onset infection, all-NICU admissions, necrotizing enterocolitis, respiratory distress, and very preterm screening cohorts. Across settings and timepoints, nSOFA demonstrated consistent, good-to-excellent mortality discrimination, with reported AUROCs ≥ 0.80 and upper ranges near 0.90–0.92; serial scoring within the first 6–12 h generally improved risk classification. Disease-specific applications (NEC, early-onset infection) showed similar discrimination for death or composite adverse outcomes. Conclusions: Evidence from diverse NICU contexts indicates that nSOFA is a pragmatic, EHR-ready organ dysfunction score with robust discrimination for mortality and serious morbidity, supporting routine, serial use for risk stratification and standardized endpoints in neonatal sepsis pathways, aligned with contemporary organ dysfunction–based pediatric criteria. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

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9 pages, 1528 KB

Open AccessBrief Report

Impact of Deep Learning-Based Reconstruction on the Accuracy and Precision of Cardiac Tissue Characterization

by Margarita Gorodezky, Linda Reichardt, Tom Geisler, Marc-André Weber, Felix G. Meinel and Ann-Christin Klemenz

Diagnostics 2026, 16(2), 348; https://doi.org/10.3390/diagnostics16020348 - 21 Jan 2026

Cited by 1 | Viewed by 425

Abstract

Background/Objectives: Interest in myocardial mapping for cardiac MRI has increased, enabling differentiation of various cardiac diseases through T1, T2, and T2* mapping. This study evaluates the impact of deep learning (DL)-based image reconstruction on the mean and standard deviation (SD) of these techniques. [...] Read more.

Background/Objectives: Interest in myocardial mapping for cardiac MRI has increased, enabling differentiation of various cardiac diseases through T1, T2, and T2* mapping. This study evaluates the impact of deep learning (DL)-based image reconstruction on the mean and standard deviation (SD) of these techniques. Methods: Fifty healthy adults underwent cardiac MRI, with images reconstructed using the AIR Recon DL prototype. This DL approach, which reduces noise and enhances image quality, was applied at three levels and compared to non-DL reconstructions. Results: Analysis focused on the septum to minimize artifacts. For T1 mapping, mean values were 988 ± 50, 981 ± 45, 982 ± 43, and 980 ± 24 ms; for T2 mapping, mean values were 53 ± 5, 54 ± 5, 54 ± 5, and 54 ± 5 ms and for T2* mapping, mean values were 37 ± 5, 37 ± 5, 37 ± 5, and 38 ± 5 ms for no DL and increasing DL levels, respectively. Results showed no significant differences in mean values for any mappings between non-DL and DL reconstructions. However, DL significantly reduced the SD within regions of interest for T1 mapping, enhancing image sharpness and registration accuracy. No significant SD reduction was observed for T2 and T2* mappings. Conclusions: These findings suggest that DL-based reconstructions improve the precision of T1 mapping without affecting mean values, supporting their clinical integration for more accurate cardiac disease diagnosis. Future studies should include patient cohorts and optimized protocols to further validate these findings. Full article

(This article belongs to the Special Issue 3rd Edition: AI/ML-Based Medical Image Processing and Analysis)

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42 pages, 1430 KB

Open AccessReview

Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors

by Carol Yen, John W. Epling, Michelle Rockwell and Monifa Vaughn-Cooke

Diagnostics 2026, 16(2), 347; https://doi.org/10.3390/diagnostics16020347 - 21 Jan 2026

Cited by 1 | Viewed by 1384

Abstract

Diagnostic errors have been a critical concern in healthcare, leading to substantial financial burdens and serious threats to patient safety. The Improving Diagnosis in Health Care report by the National Academies of Sciences, Engineering, and Medicine (NASEM) defines diagnostic errors, focusing on accuracy, [...] Read more.

Diagnostic errors have been a critical concern in healthcare, leading to substantial financial burdens and serious threats to patient safety. The Improving Diagnosis in Health Care report by the National Academies of Sciences, Engineering, and Medicine (NASEM) defines diagnostic errors, focusing on accuracy, timeliness, and communication, which are influenced by clinical knowledge and the broader healthcare system. This review aims to integrate existing literature on diagnostic error from a systems-based perspective and examine the factors across various domains to present a comprehensive picture of the topic. A narrative literature review was structured upon the Systems Engineering Initiative for Patient Safety (SEIPS) model that focuses on six domains central to the diagnostic process: Diagnostic Team Members, Tasks, Technologies and Tools, Organization, Physical Environment, and External Environment. Studies on contributing factors for diagnostic error in these domains were identified and integrated. The findings reveal that the effectiveness of diagnostics is influenced by complex, interconnected factors spanning all six SEIPS domains. In particular, socio-behavioral factors, such as team communication, cognitive bias, and workload, and environmental pressures, stand out as significant but difficult-to-capture contributors in traditional and commonly used data resources like electronic health records (EHRs), which limits the scope of many studies on diagnostic errors. Factors associated with diagnostic errors are often interconnected across healthcare system stakeholders and organizations. Future research should address both technical and behavioral elements within the diagnostic ecosystem to reduce errors and enhance patient outcomes. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

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16 pages, 2619 KB

Open AccessArticle

Multiparametric Ultrasound Features of the Diffuse Sclerosing Variant of Papillary Thyroid Carcinoma: A Single-Center Case Series

by Monica Latia, Stefania Bunceanu, Andreea Bena, Octavian Constantin Neagoe and Dana Stoian

Diagnostics 2026, 16(2), 346; https://doi.org/10.3390/diagnostics16020346 - 21 Jan 2026

Viewed by 699

Abstract

Background/Objectives: The diffuse sclerosing variant of papillary thyroid carcinoma (DSV-PTC) is a rare and aggressive subtype characterized by diffuse gland involvement and early cervical lymph node metastasis. Preoperative differentiation from classic papillary thyroid carcinoma and autoimmune thyroid disease remains challenging on B-mode ultrasound. [...] Read more.

Background/Objectives: The diffuse sclerosing variant of papillary thyroid carcinoma (DSV-PTC) is a rare and aggressive subtype characterized by diffuse gland involvement and early cervical lymph node metastasis. Preoperative differentiation from classic papillary thyroid carcinoma and autoimmune thyroid disease remains challenging on B-mode ultrasound. This study aimed to describe the multiparametric ultrasound features of DSV-PTC in a single-center case series and highlight practical imaging insights. Methods: We retrospectively reviewed seven consecutive patients with histologically confirmed DSV-PTC evaluated at a single center between 2013 and 2025. All patients underwent standardized B-mode ultrasound, color Doppler, and two-dimensional shear-wave elastography prior to surgery. Clinical, autoimmune, cytological, surgical, pathological, and follow-up data were analyzed descriptively. Results: The cohort included five females and two males (mean age 28 years). Autoimmune thyroid disease was present in three patients. High-risk ultrasound features were identified in all cases, with microcalcifications in six patients and a diffuse “snowstorm” appearance in five. Elastography demonstrated increased stiffness in six out of seven lesions (Emean 28–173 kPa; Emax 31–300 kPa). Cervical lymph node metastases were confirmed in all patients. In two cases, elastography aided identification of focal malignant involvement within diffusely altered thyroid parenchyma. All patients underwent total thyroidectomy with central neck dissection; lateral neck dissection and radioiodine therapy were performed selectively. No distant metastases were detected. Conclusions: In this case series, DSV-PTC showed a characteristic multiparametric ultrasound pattern combining high-risk B-mode features with frequently increased tissue stiffness. Elastography provided complementary information, particularly in the presence of autoimmune thyroid disease, by helping localize focal malignant involvement within diffusely altered parenchyma. Full article

(This article belongs to the Special Issue Thyroid Cancer: Types, Symptoms, Diagnosis and Management)

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12 pages, 612 KB

Open AccessArticle

Prognostic Performance of the Korean Triage and Acuity Scale Combined with the National Early Warning Score for Predicting Mortality and ICU Admission at Emergency Department Triage: A Retrospective Observational Study

by Jungtaek Park, Sang Hoon Oh, Ae Kyung Gong, Jee Yong Lim, Sun Hee Woo, Won Jung Jeong, Ji Hoon Kim, In Soo Kim and Soo Hyun Kim

Diagnostics 2026, 16(2), 345; https://doi.org/10.3390/diagnostics16020345 - 21 Jan 2026

Viewed by 445

Abstract

Objectives: This study aimed to compare the predictive performance of the Korean Triage and Acuity Scale (KTAS) and the National Early Warning Score (NEWS) for serious adverse events (SAEs), including mortality and intensive care unit (ICU) admission, during emergency department (ED) stay. [...] Read more.

Objectives: This study aimed to compare the predictive performance of the Korean Triage and Acuity Scale (KTAS) and the National Early Warning Score (NEWS) for serious adverse events (SAEs), including mortality and intensive care unit (ICU) admission, during emergency department (ED) stay. We also evaluated whether combining the two systems improves prediction accuracy. Methods: This retrospective study included adult patients (≥19 years) who presented to a university-affiliated ED between October and December 2024. KTAS and NEWS were assessed simultaneously at triage. NEWS2 was calculated retrospectively based on routinely documented vital signs and medical history without performing routine arterial blood gas analysis. The primary outcome was the occurrence of SAE during the ED stay. Predictive performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC), and logistic regression models were used to identify independent associations. Results: A total of 4216 patients were analyzed, of whom 255 (6.0%) experienced SAEs. All three scores—KTAS, NEWS and NEWS2—were independently associated with the occurrence of SAEs. The AUCs for KTAS, NEWS and NEWS2 were 0.75 (95% CI, 0.74–0.76), 0.73 (95% CI, 0.71–0.74) and 0.73 (95% CI, 0.71–0.74), respectively. Combining KTAS with NEWS or NEWS2 significantly improved predictive accuracy (AUC 0.81, 95% CI 0.79–0.82; p < 0.001). Conclusions: Both KTAS and NEWS/NEWS2 reliably predicted in-ED adverse outcomes, and their combination further enhanced prognostic performance. Integrating physiology-based early warning scores with structured triage systems may help identify high-risk ED patients earlier and optimize resource allocation. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

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12 pages, 1014 KB

Open AccessArticle

A Diagnostic Algorithm for Reconstructing the Direction of Gunshots Using OsiriX and Maya in Living Patients: A Forensic Radiology Approach

by Ginevra Malta, Stefania Zerbo, Tommaso D’Anna, Simona Pellerito, Antonina Argo, Mauro Midiri, Giuseppe Lo Re, Francesca Licitra and Angelo Montana

Diagnostics 2026, 16(2), 344; https://doi.org/10.3390/diagnostics16020344 - 21 Jan 2026

Viewed by 322

Abstract

Background/Objectives: Gunshot wounds in living patients present significant challenges from both a clinical and a forensic perspective. Understanding the exact trajectory of a bullet is crucial not only for guiding treatment but also for providing reliable documentation in legal settings. This work introduces [...] Read more.

Background/Objectives: Gunshot wounds in living patients present significant challenges from both a clinical and a forensic perspective. Understanding the exact trajectory of a bullet is crucial not only for guiding treatment but also for providing reliable documentation in legal settings. This work introduces a practical diagnostic workflow that combines OsiriX (V. 14.1.1), a DICOM viewer with advanced 3D tools, with Autodesk Maya, a modeling platform used to recreate the external shooting scene. Methods: CT scans obtained with multidetector systems were analyzed in OsiriX using a structured, seven-step process that included multiplanar reconstructions, 3D renderings, and region-of-interest tracking. The reconstructed trajectories were then exported to Maya, where they were integrated into a virtual model of the shooting scene to correlate internal findings with the incident’s external dynamics. Results: The workflow allowed precise identification of entry and exit points, reliable reconstruction of bullet paths, and effective 3D visualization. While OsiriX provided detailed information for clinical and radiological purposes, the use of Maya enabled simulation of the external scene, improving forensic interpretation and courtroom presentation. The procedure proved reproducible across cases and compatible with emergency timelines. Conclusions: The combined use of OsiriX and Maya offers a reproducible and informative method for analyzing gunshot wounds in living patients. This approach not only supports surgical and diagnostic decisions but also enhances the forensic value of radiological data by linking internal trajectories to external shooting dynamics. Its integration into trauma imaging protocols and forensic workflows could represent a significant step toward standardized ballistic documentation. Full article

(This article belongs to the Special Issue Advances in Pathology for Forensic Diagnosis)

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15 pages, 2430 KB

Open AccessArticle

Improved Detection of Small (<2 cm) Hepatocellular Carcinoma via Deep Learning-Based Synthetic CT Hepatic Arteriography: A Multi-Center External Validation Study

by Jung Won Kwak, Sung Bum Cho, Ki Choon Sim, Jeong Woo Kim, In Young Choi and Yongwon Cho

Diagnostics 2026, 16(2), 343; https://doi.org/10.3390/diagnostics16020343 - 21 Jan 2026

Viewed by 486

Abstract

Background/Objectives: Early detection of hepatocellular carcinoma (HCC), particularly small lesions (<2 cm), which is crucial for curative treatment, remains challenging with conventional liver dynamic computed tomography (LDCT). We aimed to develop a deep learning algorithm to generate synthetic CT during hepatic arteriography (CTHA) [...] Read more.

Background/Objectives: Early detection of hepatocellular carcinoma (HCC), particularly small lesions (<2 cm), which is crucial for curative treatment, remains challenging with conventional liver dynamic computed tomography (LDCT). We aimed to develop a deep learning algorithm to generate synthetic CT during hepatic arteriography (CTHA) from non-invasive LDCT and evaluate its lesion detection performance. Methods: A cycle-consistent generative adversarial network with an attention module [Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (U-GAT-IT)] was trained using paired LDCT and CTHA images from 277 patients. The model was validated using internal (68 patients, 139 lesions) and external sets from two independent centers (87 patients, 117 lesions). Two radiologists assessed detection performance using a 5-point scale and the detection rate. Results: Synthetic CTHA significantly improved the detection of sub-centimeter (<1 cm) HCCs compared with LDCT in the internal set (69.6% vs. 47.8%, p < 0.05). This improvement was robust in the external set; synthetic CTHA detected a greater number of small lesions than LDCT. Quantitative metrics (structural similarity index measure and peak signal-to-noise ratio) indicated high structural fidelity. Conclusions: Deep-learning–based synthetic CTHA significantly enhanced the detection of small HCCs compared with standard LDCT, offering a non-invasive alternative with high detection sensitivity, which was validated across multicentric data. Full article

(This article belongs to the Special Issue 3rd Edition: AI/ML-Based Medical Image Processing and Analysis)

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16 pages, 500 KB

Open AccessArticle

Faster Diagnosis of Suspected Lower Respiratory Tract Infections: Single-Center Evidence from BIOFIRE FilmArray^® Pneumonia Panel Results vs. Conventional Culture Method

by Beatrice Silvia Orena, Lisa Cariani, Elena Tomassini, Filippo Girardi, Monica D’Accico, Alessia Pirrone, Caterina Biassoni, Daniela Girelli, Antonio Teri, Marco Tonelli, Claudia Alteri and Annapaola Callegaro

Diagnostics 2026, 16(2), 342; https://doi.org/10.3390/diagnostics16020342 - 21 Jan 2026

Viewed by 525

Abstract

Background/Objectives: Syndromic multiplex PCR assays such as BIOFIRE FilmArray^® Pneumonia (PN) panel enable rapid and simultaneous detection of bacterial and viral pathogens in respiratory specimens, improving diagnostic accuracy and patient management in lower respiratory tract infections (LRTIs). Methods: In this [...] Read more.

Background/Objectives: Syndromic multiplex PCR assays such as BIOFIRE FilmArray^® Pneumonia (PN) panel enable rapid and simultaneous detection of bacterial and viral pathogens in respiratory specimens, improving diagnostic accuracy and patient management in lower respiratory tract infections (LRTIs). Methods: In this retrospective observational study, PN panel results in 410 bronchoalveolar lavage (BAL) samples from hospitalized patients with suspected pneumonia were analyzed and compared with those obtained using the conventional culture (CC) method. Results: The PN panel showed an overall positivity rate of 54%, detecting bacteria in 39.0% of samples, viruses in 7.1%, and atypical bacteria in 2.2%. Using the conventional culture (CC) method, 33.9% of samples tested positive. Overall, 83 (20.2%) samples that were positive by the PN panel were negative by CC, whereas only 14 specimens (3.4%) were positive by CC and negative by PN panel. The most frequently detected pathogen by both the PN panel and CC was Staphylococcus aureus (n = 67, 16.34% for PN; n = 40, 9.76% for CC). Regarding diagnostic performance, the PN panel demonstrated a sensitivity of 89.02%, a specificity of 97.86%, and an overall accuracy of 97.63%. Lower sensitivity values were observed only for the Enterobacter cloacae complex (57.14%) and the Klebsiella pneumoniae group (75%). Specificity exceeded 92% for all bacterial targets. Conclusions: The PN panel confirms enhanced pathogen detection and a shortened time-to-result. It serves as a valuable adjunct for the timely diagnosis of LRTIs, supporting antimicrobial stewardship through more precise and appropriate antibiotic selection. Full article

(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)

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17 pages, 651 KB

Open AccessReview

Intra-Arterial Radioligand Therapy in Brain Cancer: Bridging Nuclear Medicine and Interventional Neuroradiology

by Federico Sabuzi, Luca Filippi, Mariafrancesca Trulli, Fabio Domenici, Francesco Garaci and Valerio Da Ros

Diagnostics 2026, 16(2), 341; https://doi.org/10.3390/diagnostics16020341 - 21 Jan 2026

Viewed by 666

Abstract

Recurrent brain tumors—including high-grade gliomas, brain metastases, and aggressive meningiomas—continue to carry a poor prognosis, with high mortality despite therapeutic advances. The aim of this narrative review is to summarize and critically discuss the current evidence on the role of intra-arterial radioligand therapy [...] Read more.

Recurrent brain tumors—including high-grade gliomas, brain metastases, and aggressive meningiomas—continue to carry a poor prognosis, with high mortality despite therapeutic advances. The aim of this narrative review is to summarize and critically discuss the current evidence on the role of intra-arterial radioligand therapy (RLT) in the treatment of recurrent brain tumors. RLT, a targeted form of radionuclide therapy, has gained increasing attention for its potential theranostic applications in neuro-oncology. A literature search was conducted using PubMed and Scopus, including clinical studies evaluating intra-arterial radioligand delivery in central nervous system tumors. Recent research has explored intra-arterial administration of radioligands targeting somatostatin receptors and prostate-specific membrane antigen (PSMA). Somatostatin receptors are overexpressed in meningiomas, while PSMA is highly expressed in the neovasculature of glioblastomas and brain metastases; both targets can be addressed using lutetium-177 (¹⁷⁷Lu)- or actinium-225 (²²⁵Ac)-labeled radiopharmaceuticals, traditionally delivered intravenously. Available evidence indicates that the intra-arterial route achieves markedly higher radionuclide uptake on ⁶⁸Ga-PSMA-11 and ⁶⁸Ga-DOTATOC PET, as well as increased absorbed doses in dosimetric models. Dosimetric analyses consistently show greater tracer accumulation compared with intravenous administration, without evidence of significant peri-procedural toxicity. Uptake in healthy brain tissue is minimal, and no relevant differences have been reported in liver or salivary gland accumulation between intra-arterial and intravenous RLT. Although based on heterogeneous and limited data, intra-arterial RLT appears to be a promising therapeutic strategy for recurrent brain tumors. Future research should focus on improving radioligand delivery beyond the blood–brain barrier and enhancing effective tumor targeting. Full article

(This article belongs to the Special Issue PET/CT Imaging in Oncology: Clinical Advances and Perspectives)

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14 pages, 239 KB

Open AccessArticle

Predicting Hemodynamic Fluctuations During Adrenalectomy for Pheochromocytoma

by Marina Stojanovic, Magdalena Grujanic, Anka Toskovic, Milan Jovanovic, Biljana Milicic, Matija Buzejic, Branislav Rovcanin, Boban Stepanovic and Vladan Zivaljevic

Diagnostics 2026, 16(2), 340; https://doi.org/10.3390/diagnostics16020340 - 21 Jan 2026

Viewed by 361

Abstract

Background: Pheochromocytoma is a rare adrenal neuroendocrine tumor characterized by excessive catecholamine secretion, which can lead to significant perioperative hemodynamic instability. Despite advances in anesthetic and surgical management, intraoperative hypotension is a common complication. This study aimed to identify preoperative and intraoperative predictors [...] Read more.

Background: Pheochromocytoma is a rare adrenal neuroendocrine tumor characterized by excessive catecholamine secretion, which can lead to significant perioperative hemodynamic instability. Despite advances in anesthetic and surgical management, intraoperative hypotension is a common complication. This study aimed to identify preoperative and intraoperative predictors of hemodynamic instability during adrenalectomy for pheochromocytoma in order to improve intraoperative management and patient safety. Methods: This retrospective study included adult patients who underwent adrenalectomy for pheochromocytoma at the University Clinical Center of Serbia between January 2022 and June 2025. Preoperative clinical and biochemical data, tumor characteristics evaluated by imaging methods (CT or MRI), surgical approach, and intraoperative hemodynamic parameters were analyzed. Intraoperative hypotension was defined as mean arterial pressure (MAP) < 60 mmHg despite adequate volume resuscitation. Univariate and multivariate logistic regression analyses were performed to identify predictors of hypotension. Results: A total of 51 adult patients were included in the analysis. Intraoperative hypotension occurred in 26 patients (51%) and was significantly associated with larger tumor size and increased intraoperative fluid requirements. Multivariate analysis identified tumor diameter ≥ 49 mm (OR 0.176, 95% CI 0.034–0.895, p = 0.036) and intraoperative crystalloid infusion ≥ 1200 mL/h (OR 0.132, 95% CI 0.030–0.574, p = 0.007) as independent predictors of intraoperative hypotension. Preoperative catecholamine levels, surgical approach, and type of alpha-blocker were not significant predictors. Conclusions: Tumor size was identified as a significant predictor of intraoperative hemodynamic instability during adrenalectomy for pheochromocytoma. Careful preoperative assessment and individualized intraoperative fluid management may help reduce the risk of hypotension and optimize perioperative outcomes. Full article

(This article belongs to the Special Issue State of the Art in the Diagnosis and Management of Endocrine Tumors)

15 pages, 3520 KB

Open AccessArticle

Male Breast Cancer in a Bronx Urban Population: A Single-Institution Retrospective Observational Study

by Kristen Lee, Bhakti Patel, Ruth Samson, Emily Hunt, Christian L. Sellers and Takouhie Maldjian

Diagnostics 2026, 16(2), 339; https://doi.org/10.3390/diagnostics16020339 - 21 Jan 2026

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Abstract

Background/Objectives: This study seeks to evaluate the clinical characteristics of newly diagnosed male breast cancers within the traditionally underserved Bronx population at risk for poorer health outcomes. Methods: We retrospectively searched our database for male patients who presented for mammographic evaluation [...] Read more.

Background/Objectives: This study seeks to evaluate the clinical characteristics of newly diagnosed male breast cancers within the traditionally underserved Bronx population at risk for poorer health outcomes. Methods: We retrospectively searched our database for male patients who presented for mammographic evaluation between 1 January 2016 and 1 October 2024. The primary outcomes were the prevalence of biopsy-proven male breast cancer and its association with gynecomastia and TNM stage at diagnosis. Clinical data, including TNM staging, receptor status, risk factors, and patient demographics, were recorded for patients with biopsy-proven breast cancer based on biopsy results. Two dedicated breast imagers retrospectively evaluated mammograms of these patients to determine by consensus the presence of gynecomastia. Analyses were descriptive in nature. Results: During the study period, 423 screening mammograms and 1775 diagnostic mammograms were performed on male patients. Twenty-six male patients with biopsy-proven breast cancer were identified (two were bilateral and four were multifocal). In total, 69% of our male breast cancer patients (18 out of 26) demonstrated gynecomastia, which was similar across demographic groups, ranging from 63 to 75%. Out of the three patients with Stage 4 disease, two were Black and one was White. Stage 3 or higher disease was seen in 29% of our Black patients, 12% of our White patients, and 0% of our Hispanic patients. Conclusions: Male breast cancer in this Bronx population was frequently associated with gynecomastia and showed notable demographic disparities. Black patients presented with more advanced disease than other demographic groups. These descriptive findings highlight areas of further investigation and may help inform future outreach and early detection efforts in high-risk, underserved communities. This retrospective, single-institution analysis was limited by a small sample size and did not include formal statistical testing; therefore, the findings are descriptive and warrant validation with larger cohorts. Full article

(This article belongs to the Special Issue Diagnosis, Prognosis and Management of Breast Cancer)

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13 pages, 1160 KB

Open AccessArticle

Centrifugation Versus Centrifugation-Free Stool Processing: Can the Simple One-Step Method Reliably Diagnose Pediatric Pulmonary Tuberculosis Using Xpert MTB/RIF Ultra?

by S. M. Mazidur Rahman, Senjuti Kabir, Sabrina Choudhury, Sohag Miah, Tanjina Rahman, Md. Jahid Hasan, Mohammad Khaja Mafij Uddin, Arifa Nazneen, Shahriar Ahmed, Aung Kya Jai Maug and Sayera Banu

Diagnostics 2026, 16(2), 338; https://doi.org/10.3390/diagnostics16020338 - 21 Jan 2026

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Abstract

Background/Objectives: Stool-based GeneXpert testing has become a useful approach for diagnosing pediatric pulmonary tuberculosis (PTB). This study compared two stool-processing methods, centrifugation-based processing (CBP) and simple one-step (SOS), for detecting PTB in children using Xpert MTB/RIF Ultra (Ultra). Methods: Children with [...] Read more.

Background/Objectives: Stool-based GeneXpert testing has become a useful approach for diagnosing pediatric pulmonary tuberculosis (PTB). This study compared two stool-processing methods, centrifugation-based processing (CBP) and simple one-step (SOS), for detecting PTB in children using Xpert MTB/RIF Ultra (Ultra). Methods: Children with presumptive PTB were screened cross-sectionally, and stool samples were collected and tested with Ultra using the CBP method from March 2022 to December 2024 across seven divisions of Bangladesh. A subset of stool samples (n = 281) that tested positive (n = 191) and negative (n = 90) by the CBP method were re-tested again with the same sample by Ultra using the SOS method. The results of the Ultra with SOS-processed stool were compared with the CBP method to evaluate overall agreement and detection efficiency across different bacterial burdens. Results: The SOS method detected 97 of 191 CBP-positive samples, resulting in a positive percentage agreement of 50.8% (95% CI: 43.5–58.1). All 90 Ultra-negative stool were also negative by the SOS method, yielding a negative percentage agreement of 100% (95% CI: 96.0–100.0). Overall agreement between the methods was 66.6% (Kappa: 0.398). The SOS method detected 100% of high- (4/4) and medium- (7/7), 97.3% (36/37) of low-, and 83.3% (35/42) of very-low-bacterial-burden samples, but only 14.9% (15/101) of the trace-detected samples that were identified by the CBP method. Conclusions: Stool testing with Ultra using the SOS processing method missed a significant number of the most prevalent form of child TB—the ‘trace-detected’ category identified by the CBP method. For increased detection of childhood TB nationwide, the national program should prioritize the use of Ultra on stool samples processed by the CBP method. Full article

(This article belongs to the Special Issue Tuberculosis Detection and Diagnosis 2025)

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