Diagnostics

18 pages, 662 KiB

Open AccessArticle

Assessing Sternal Dimensions for Sex Classification: Insights from a Greek Computed Tomography-Based Study

by Konstantina Vatzia, Michail Fanariotis, Maciej Bugajski, Ioannis V. Fezoulidis, Maria Piagkou, Marianna Vlychou, George Triantafyllou, Ioannis Vezakis, George Botis, Stavroula Papadodima, George Matsopoulos and Katerina Vassiou

Diagnostics 2025, 15(13), 1649; https://doi.org/10.3390/diagnostics15131649 (registering DOI) - 27 Jun 2025

Abstract

Background/Objectives: This study aimed to assess the potential of sternal morphometric parameters derived from multidetector computed tomography (MDCT) for sex estimation in a contemporary Greek population. A secondary objective was to develop and evaluate statistical and machine learning models based on these [...] Read more.

Background/Objectives: This study aimed to assess the potential of sternal morphometric parameters derived from multidetector computed tomography (MDCT) for sex estimation in a contemporary Greek population. A secondary objective was to develop and evaluate statistical and machine learning models based on these measurements for forensic identification. Methods: Sternal measurements were obtained from chest MDCT scans of 100 Greek adults (50 males, 50 females). Morphometric variables included total sternum length, surface area, angle, and index (SL, SSA, SA, and SI); manubrium length, width, thickness, and index (MBL, MBW, MBT, and MBI); sternal body length, width, thickness, and index (SBL, SBW, SBT, and SBI); and xiphoid process length and thickness (XPL and XPT). Logistic regression and a Random Forest classifier were applied to assess the predictive accuracy of these parameters. Results: Both models showed high classification performance. Logistic regression identified MBL and SBL as the most predictive variables, yielding 91% overall accuracy, with 92% sensitivity and 90% specificity. The Random Forest model achieved comparable results (91% accuracy, 88% sensitivity, 93% specificity), ranking SSA as the most influential feature. Conclusions: MDCT-derived sternal morphometry provides a reliable, non-invasive method for sex estimation. Parameters such as MBL, SBL, and SSA demonstrate strong discriminatory power and support the development of population-specific standards for forensic applications. Full article

(This article belongs to the Special Issue New Perspectives in Forensic Diagnosis)

25 pages, 418 KiB

Open AccessReview

Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment

by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan and Alireza Tavakkoli

Diagnostics 2025, 15(13), 1648; https://doi.org/10.3390/diagnostics15131648 (registering DOI) - 27 Jun 2025

Abstract

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a [...] Read more.

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)

13 pages, 427 KiB

Open AccessArticle

The Diagnosis of and Preoperative Planning for Rapidly Osteoarthritis of the Hip: The Role of Sagittal Spinopelvic Geometry and Anterior Acetabular Wall Deficiency—A Prospective Observational Study

by Andrei Oprișan, Andrei Marian Feier, Zuh SandorGyorgy, Octav Marius Russu and Tudor Sorin Pop

Diagnostics 2025, 15(13), 1647; https://doi.org/10.3390/diagnostics15131647 (registering DOI) - 27 Jun 2025

Abstract

Background/Objectives:Rapidly progressive osteoarthritis of the hip (RPOH) has unique diagnostic and surgical challenges due to rapid joint degeneration and acetabular structural alterations. This study aimed to investigate correlations between preoperative spinopelvic geometry and anterior acetabular wall bone stock deficiency in RPOH patients [...] Read more.

Background/Objectives:Rapidly progressive osteoarthritis of the hip (RPOH) has unique diagnostic and surgical challenges due to rapid joint degeneration and acetabular structural alterations. This study aimed to investigate correlations between preoperative spinopelvic geometry and anterior acetabular wall bone stock deficiency in RPOH patients and introduce an advanced imaging measurement techniques for cases with amputated femoral heads. Methods: A prospective observational study was conducted that enrolled 85 patients, comprising 40 with unilateral RPOH (Zazgyva Grade II or III) and 45 controls with primary osteoarthritis (OA). Preoperative spino-pelvic parameters (pelvic tilt—PT, sacral slope—SS, lumbar lordosis—LL, and T1 pelvic angle) and acetabular anterior wall characteristics (anterior center edge angle—ACEA, anterior wall index—AWI, and anterior acetabular surface area—AASA) were measured using standardized radiographic and CT imaging protocols, including a new methodology for acetabular center estimation in femoral head-amputated cases. Results: Significant differences were identified between RPOH and primary OA patients in the PT (22.5° vs. 18.9°, p = 0.032), SS (37.8° vs. 41.1°, p = 0.041), T1 pelvic angle (14.3° vs. 11.8°, p = 0.018), and anterior center edge angle (25.3° vs. 29.7°, p = 0.035). RPOH patients exhibited pronounced spinopelvic misalignment and anterior acetabular deficiencies. Conclusions: RPOH is associated with spinopelvic misalignment and anterior acetabular wall deficiency. Accurate preoperative diagnosis imaging and personalized surgical approaches specifically addressing acetabular bone stock deficiencies are mandatory in these cases. Full article

(This article belongs to the Special Issue Diagnosis and Management of Osteoarthritis)

16 pages, 2584 KiB

Open AccessArticle

Radiologic Predictors of Disease Recurrence in Nasopharyngeal Carcinoma: A Retrospective Evaluation of MRI and ¹⁸F-FDG-PET/CT Parameters

by Banu Karaalioğlu, Tansel Çakır, Ömer Yazıcı, Mustafa S. Tekin and Ebru Karcı

Diagnostics 2025, 15(13), 1646; https://doi.org/10.3390/diagnostics15131646 (registering DOI) - 27 Jun 2025

Abstract

Background/Objectives: NPC is a radiosensitive malignancy with high recurrence rates despite therapeutic advances. This study aimed to identify radiologic and metabolic predictors of recurrence in newly diagnosed NPC by integrating MRI and ¹⁸F-FDG PET/CT parameters. Methods: Fifty-two patients with biopsy-proven, previously untreated [...] Read more.

Background/Objectives: NPC is a radiosensitive malignancy with high recurrence rates despite therapeutic advances. This study aimed to identify radiologic and metabolic predictors of recurrence in newly diagnosed NPC by integrating MRI and ¹⁸F-FDG PET/CT parameters. Methods: Fifty-two patients with biopsy-proven, previously untreated NPC who underwent pre-treatment MRI and ¹⁸F-FDG PET/CT were retrospectively analyzed. Local tumor features, nodal status, and response patterns were evaluated using MRI and PET/CT-derived metrics: SUVmax, SUVmean, SUVpeak, MTV, and TLG. The post-treatment MRI response was categorized into six patterns. Univariate and multivariate analyses were performed to identify independent predictors. Results: Recurrence occurred in 27% of patients. Based on the multivariate analysis, PNI, extensive PPS invasion, GTV, and metastatic LN count were identified as independent predictors of recurrence (PNI: OR = 1.60, p = 0.029; PPS: OR = 1.23, p = 0.027; GTV: OR = 1.08, p = 0.042; LN count: OR = 1.20, p = 0.031). PNI and PPS invasion were significantly associated with local failure (HR = 8.21, p = 0.008 and HR = 3.52, p = 0.043, respectively). GTV was independently associated with an increased risk of local (HR = 1.14, p = 0.021) and distant recurrence (HR = 1.19, p = 0.024). The presence of metastatic disease at diagnosis (HR = 6.27, p = 0.027) and a higher LN count (HR = 1.17, p = 0.028) were also linked to distant failure. Conclusions: Imaging-derived predictors including GTV, PNI, LN burden, and MRI-based response patterns demonstrate prognostic value for disease recurrence in NPC and may guide risk-adapted treatment strategies. Full article

(This article belongs to the Special Issue Advances in the Diagnosis and Management of Head and Neck Disease)

► Show Figures

Figure 1

11 pages, 768 KiB

Open AccessBrief Report

Differences in Imaging and Histology Between Sinonasal Inverted Papilloma with and Without Squamous Cell Carcinoma

by Niina Kuusisto, Jaana Hagström, Goran Kurdo, Aaro Haapaniemi, Antti Markkola, Antti Mäkitie and Markus Lilja

Diagnostics 2025, 15(13), 1645; https://doi.org/10.3390/diagnostics15131645 (registering DOI) - 27 Jun 2025

Abstract

Objectives: Sinonasal inverted papilloma (SNIP) is a rare benign tumor that has potential for malignant transformation, usually into squamous cell carcinoma (SCC). The pre-operative differentiation between SNIP and SNIP-SCC is essential in determining the therapeutic strategy, but it is a challenge, as biopsies [...] Read more.

Objectives: Sinonasal inverted papilloma (SNIP) is a rare benign tumor that has potential for malignant transformation, usually into squamous cell carcinoma (SCC). The pre-operative differentiation between SNIP and SNIP-SCC is essential in determining the therapeutic strategy, but it is a challenge, as biopsies may fail to recognize the malignant part of the tumor. Further, a SNIP can also be locally aggressive and thus mimic a malignant tumor. This retrospective study compares the pre-operative differences in computed tomography (CT) and histologic findings between patients with a benign SNIP and those with a SNIP-SCC. Methods: Eight patients with SNIP-SCC were selected from the hospital registries of the Department of Otorhinolaryngology, Helsinki University Hospital (Helsinki, Finland). For each case a comparable SNIP case without malignancy was selected. Five histopathologic samples of both the SNIP and SNIP-SCC tumors were retrieved. CT images and the histopathologic samples were re-evaluated by two observers. Results: The nasal cavity and ethmoid and maxillary sinuses were the most common sites for both tumor types. The SNIP tumors were mostly unilateral, and the SNIP-SCC tumors were both unilateral and bilateral. Only SNIP-SCC tumors demonstrated bone defects and orbital or intracranial invasion. Dysplastic findings such as dyskeratosis, nuclear atypia, and maturation disturbances were seen only in the SNIP-SCC tumors. Conclusions: Bony destruction and invasion of adjacent structures in pre-operative CT images seem to be pathognomonic signs of SNIP-SCC based on this series. To differentiate between SNIP and SNIP-SCC tumors all available pre-operative investigations are warranted. Full article

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

15 pages, 256 KiB

Open AccessArticle

Systemic and Local Immunological Markers in Preeclampsia

by Almagul Kurmanova, Altynay Nurmakova, Damilya Salimbayeva, Gulfiruz Urazbayeva, Gaukhar Kurmanova, Natalya Kravtsova, Zhanar Kypshakbayeva and Madina Khalmirzaeva

Diagnostics 2025, 15(13), 1644; https://doi.org/10.3390/diagnostics15131644 (registering DOI) - 27 Jun 2025

Abstract

Preeclampsia (PE) is one of the main causes of obstetric complications and leads to both maternal and neonatal mortality. The maternal innate immune system plays an important role throughout pregnancy by providing protection against pathogens, while simultaneously inducing tolerance to a semi-allogenic developing [...] Read more.

Preeclampsia (PE) is one of the main causes of obstetric complications and leads to both maternal and neonatal mortality. The maternal innate immune system plays an important role throughout pregnancy by providing protection against pathogens, while simultaneously inducing tolerance to a semi-allogenic developing fetus and placental development. Background/Objectives: To conduct a comparative study of immunological markers in the blood and placenta in preeclampsia. Methods: A total of 35 pregnant women were enrolled in a comparative study with preeclampsia (7) and with physiological pregnancy (28). A study of the immune status in peripheral blood and placenta was conducted with an examination of the subpopulation of lymphocytes profile and intracellular cytokines production by flow cytometry. Results: In the blood of pregnant women with PE, there was a decrease in CD14+ monocytes, as well as a significant increase of natural killers CD16+, CD56+ and activation markers HLA-DR+ and CD95+, as well as a significant rise in production of IL-10, TNF, Perforin, GM-CSF, and IGF. At the same time, in placental tissue in patients with preeclampsia, on the contrary, a significant decrease in regulatory cells CD4+, CD8+, CD14+, CD56+, CD59+, activation markers CD95+, as well as anti-inflammatory cytokine IL-10, growth factors VEGFR and IGF was detected. Conclusions: The maternal–fetal immune profile is crucial for successful fetal development and dysregulation of T-, B-, and NK cells can contribute to inflammation, oxidative stress, and the development of preeclampsia. Full article

(This article belongs to the Special Issue New Insights into Maternal-Fetal Medicine: Diagnosis and Management)

12 pages, 1928 KiB

Open AccessArticle

Retention of Asymptomatic Impacted Third Molars: Effects on Alveolar Bone at the Distal Surface of Second Molars over Time

by Ahmed Ata Alfurhud and Hesham Alouthah

Diagnostics 2025, 15(13), 1643; https://doi.org/10.3390/diagnostics15131643 (registering DOI) - 27 Jun 2025

Abstract

Objective: To assess radiographic changes in the alveolar bone on the distal aspect of the second molars (2Ms) over time, while impacted third molars (ITMs) remain present across two timepoints. Methods: This retrospective observational study aimed to assess radiographic changes between two timepoints [...] Read more.

Objective: To assess radiographic changes in the alveolar bone on the distal aspect of the second molars (2Ms) over time, while impacted third molars (ITMs) remain present across two timepoints. Methods: This retrospective observational study aimed to assess radiographic changes between two timepoints (T0 and T1). Both Orthopantomogram (OPG) and Periapical (PA) X-rays were utilized, with three measurements taken on the distal surface of 2Ms using EMAGO 6.1 software. Statistical significance was defined as a p-value < 0.05. Results: A total of 51 patients met the inclusion criteria, with a mean age of 45 years (SD ± 13). Sixty-eight second molars were assessed at baseline (T0) and follow-up (T1), with a mean interval of 20 months (SEM ± 62 days). No significant changes were found in vertical, oblique, or angular bone levels between T0 and T1. Gender significantly affected the cementoenamel junction (CEJ)–base of defect (BD) measurements (p = 0.022) and defect angles at T0 (p = 0.048), but not at the adjusted T1 (p = 0.292). Other variables, including medical history, smoking, and ITM angulation, showed no influence. Patient age was borderline significant in relation to intrabony defect angle (p = 0.047). Conclusions: Considering its limitations, this analysis does not provide evidence to support the hypothesis that prophylactic extraction of ITMs yields significant bone-sparing benefits. Furthermore, it does not establish that prolonged retention of ITMs consistently results in short-term bone alterations in adjacent 2Ms. Consequently, further research is warranted to more accurately assess the medium- to long-term implications of ITM retention on the bone levels of 2Ms. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Oral and Maxillofacial Disease: 2nd Edition)

► Show Figures

Figure 1

22 pages, 1359 KiB

Open AccessArticle

A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis

by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir and The Alzheimer’s Disease Neuroimaging Initiative

Diagnostics 2025, 15(13), 1642; https://doi.org/10.3390/diagnostics15131642 (registering DOI) - 27 Jun 2025

Abstract

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study [...] Read more.

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study proposes an explainable ensemble-based diagnostic framework trained on both clinical data and mid-slice axial MRI from the ADNI and OASIS datasets. The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decision-making process. Our mid-slice approach minimizes unnecessary features and enhances model explainability by design. Results: We achieved state-of-the-art diagnostic accuracy: 99% on OASIS and 97.61% on ADNI using clinical data alone; 99.38% on OASIS and 98.62% on ADNI using only mid-slice MRI; and 99% accuracy when combining both modalities. The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. Conclusions: This research presents a step toward more transparent AI-driven diagnostics, bridging the gap between accuracy and explainability in XAI. Full article

(This article belongs to the Special Issue Explainable Machine Learning in Clinical Diagnostics)

► Show Figures

Figure 1

15 pages, 722 KiB

Open AccessArticle

Evaluation of Interleukin-10, Vascular Endothelial Growth Factor Levels, and Bone Marrow Parameters in Multiple Myeloma Patients at Diagnosis and After Treatment

by Fulya Memis, Meryem Yalvac Kandefer, Sonay Aydin, Klara Dalva and Selami Kocak Toprak

Diagnostics 2025, 15(13), 1641; https://doi.org/10.3390/diagnostics15131641 - 27 Jun 2025

Abstract

Background: Interleukin-10 (IL-10) and vascular endothelial growth factor (VEGF) are believed to possess a role in the pathophysiology of multiple myeloma (MM). We aimed to assess the significance of these parameters in the diagnosis, monitoring, and prognosis of the disease by examining them [...] Read more.

Background: Interleukin-10 (IL-10) and vascular endothelial growth factor (VEGF) are believed to possess a role in the pathophysiology of multiple myeloma (MM). We aimed to assess the significance of these parameters in the diagnosis, monitoring, and prognosis of the disease by examining them in patients at diagnosis and post-treatment and comparing the findings with those of healthy individuals. Methods: We conducted blood sampling from 35 patients diagnosed with MM at the time of diagnosis and from 15 of these patients post-treatment. We additionally assessed similar serum markers in a control group of 15 healthy individuals. Furthermore, we documented laboratory results, organ involvement, comorbidities, and CD27-CD81 levels assessed using flow cytometry in the bone marrow, along with treatments and patient responses. We also examined the quantity of cells collected during mobilization in patients who had autologous stem cell transplantation. Results: We found a positive correlation (p = 0.028/p = 0.035) between IL-10 and VEGF with the international staging score. In patients with renal involvement, IL-10 levels were higher and VEGF levels were lower than those without renal involvement (p = 0.011/p = 0.012). We showed that VEGF levels decreased significantly with treatment (p = 0.001). We found no statistically significant correlation between treatment responses and IL-10 and VEGF. The number of CD34 cells collected by mobilization showed a negative correlation with CD27 and a positive correlation with VEGF (p = 0.007/p = 0.032). Conclusions: Serum IL-10 level is associated with ISS and renal involvement in MM patients. There is a positive correlation between serum VEGF levels and the number of stem cells collected during mobilization. As CD27 expression increases, the number of stem cells collected in mobilization decreases. Full article

(This article belongs to the Special Issue Advances in Laboratory Analysis and Diagnostics)

► Show Figures

Figure 1

12 pages, 796 KiB

Open AccessArticle

Enhancing Predictive Tools for Skeletal Growth and Craniofacial Morphology in Syndromic Craniosynostosis: A Focus on Cranial Base Variables

by Lantian Zheng, Norli Anida Abdullah, Norlisah Mohd Ramli, Nur Anisah Mohamed, Mohamad Norikmal Fazli Hisam and Firdaus Hariri

Diagnostics 2025, 15(13), 1640; https://doi.org/10.3390/diagnostics15131640 - 27 Jun 2025

Abstract

Background/Objectives: Patients with syndromic craniosynostosis (SC) pose a significant challenge for post-operational outcomes due to the variability in craniofacial deformities and gain-of-function characteristics. This study aims to develop validated predictive tools using stable cranial base variables to predict changes in the midfacial [...] Read more.

Background/Objectives: Patients with syndromic craniosynostosis (SC) pose a significant challenge for post-operational outcomes due to the variability in craniofacial deformities and gain-of-function characteristics. This study aims to develop validated predictive tools using stable cranial base variables to predict changes in the midfacial region and explore the craniofacial morphology among patients with SC. Methods: This study involved 17 SC patients under 12 years old, 17 age-matched controls for morphological analysis, and 21 normal children for developing craniofacial predictive models. A stable cranial base and changeable midfacial variables were analyzed using the Mann–Whitney U test. Pearson correlation identified linear relationships between the midface and cranial base variables. Multicollinearity was checked before fitting the data with multiple linear regression for growth prediction. Model adequacy was confirmed and the 3-fold cross-validation ensured results reliability. Results: Patients with SC exhibited a shortened cranial base, particularly in the middle cranial fossa (S-SO), and a sharper N-S-SO and N-SO-BA angle, indicating a downward rotation and kyphosis. The midface length (ANS-PNS) and zygomatic length (ZMs-ZTi) were significantly reduced, while the midface width (ZFL-ZFR) was increased. Regression models for the midface length, width, and zygomatic length were given as follows: ANS-PNS = 23.976 + 0.139 S-N + 0.545 SO-BA − 0.120 N-S-BA + 0.078 S-SO-BA + 0.051 age (R² = 0.978, RMSE = 1.058); ZFL-ZFR = −15.618 + 0.666 S-N + 0.241 N-S-BA + 0.155 S-SO-BA + 0.121 age (R² = 0.903, RMSE = 3.158); and ZMs-ZTi = −14.403 + 0.765 SO-BA + 0.266 N-S-BA + 0.111 age (R² = 0.878, RMSE = 3.720), respectively. Conclusions: The proposed models have potential applications for midfacial growth estimation in children with SC. Full article

(This article belongs to the Special Issue Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care)

► Show Figures

Figure 1

13 pages, 554 KiB

Open AccessArticle

Correlating Patient Symptoms and CT Morphology in AI-Detected Incidental Pulmonary Embolisms

by Selim Abed, Lucas Brandstetter and Klaus Hergan

Diagnostics 2025, 15(13), 1639; https://doi.org/10.3390/diagnostics15131639 - 27 Jun 2025

Abstract

Background/Objectives: Incidental pulmonary embolisms (IPEs) may be asymptomatic and radiologists may discover them for unrelated reasons, and they can thereby go underdiagnosed and undertreated. Artificial intelligence (AI) has emerged as a possible aid to radiologists in identifying IPEs. This study aimed to [...] Read more.

Background/Objectives: Incidental pulmonary embolisms (IPEs) may be asymptomatic and radiologists may discover them for unrelated reasons, and they can thereby go underdiagnosed and undertreated. Artificial intelligence (AI) has emerged as a possible aid to radiologists in identifying IPEs. This study aimed to assess the clinical and radiological significance of IPEs that a deep learning AI algorithm detected and correlate them with thrombotic burden, CT morphologic signs of right heart strain, and clinical symptoms. Methods: We retrospectively evaluated 13,603 contrast-enhanced thoracic and abdominal CT scans performed over one year at a tertiary care hospital using a CE- and FDA-cleared AI algorithm. Natural language processing (NLP) tools were used to determine whether IPEs were reported by radiologists. We scored confirmed IPEs using the Mastora, Qanadli, Ghanima, and Kirchner scores, and morphologic indicators of right heart strain and clinical parameters such as symptomatology, administered anticoagulation, and 6-month outcomes were analyzed. Results: AI identified 41 IPE cases, of which 61% occurred in oncologic patients. Most emboli were segmental, with no signs of right heart strain. Only 10% of patients were symptomatic. Thrombotic burden scores were similar between oncologic and non-oncologic groups. Four deaths occurred—all in oncologic patients. One untreated case experienced the recurrence of pulmonary embolism. Despite frequent detection, many IPEs were clinically silent. Conclusions: AI can effectively detect IPEs that are missed on initial review. However, most AI-detected IPEs are clinically silent. Integrating AI findings with morphologic and clinical criteria is crucial to avoid overtreatment and to guide appropriate management. Full article

(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)

► Show Figures

Figure 1

12 pages, 1170 KiB

Open AccessArticle

Neutrophil-Percentage-to-Albumin Ratio as a Predictor of Coronary Artery Ectasia: A Comparative Analysis with Inflammatory Biomarkers

by Mehdi Karasu and Şeyda Şahin

Diagnostics 2025, 15(13), 1638; https://doi.org/10.3390/diagnostics15131638 - 27 Jun 2025

Abstract

Background/Objectives: Coronary artery ectasia (CAE) is characterized by abnormal dilation of the coronary arteries and is associated with adverse cardiovascular events. Inflammation is believed to play a pivotal role in the development and progression of CAE. The neutrophil-percentage-to-albumin ratio (NPAR) has emerged [...] Read more.

Background/Objectives: Coronary artery ectasia (CAE) is characterized by abnormal dilation of the coronary arteries and is associated with adverse cardiovascular events. Inflammation is believed to play a pivotal role in the development and progression of CAE. The neutrophil-percentage-to-albumin ratio (NPAR) has emerged as a novel marker of systemic inflammation and may serve as a useful tool in the evaluation of CAE. This study aimed to assess the association between the NPAR and CAE and compare its predictive value to established inflammatory biomarkers, including highly sensitive C-reactive protein (hsCRP), the neutrophil-to-lymphocyte ratio (NLR), and the platelet-to-lymphocyte ratio (PLR). Methods: A retrospective analysis was conducted on 5212 patients who underwent elective coronary angiography between March 2019 and March 2023. The cohort included 165 patients with isolated CAE and 180 controls with normal coronary anatomy. Inflammatory markers and their correlation with CAE were analyzed using logistic regression models and receiver operating characteristic (ROC) analysis to determine predictive performance. Results: The NPAR was significantly elevated in the CAE group compared to the controls (p < 0.001). Multivariate analysis identified the NPAR (OR: 2.14, p = 0.003) and CRP (OR: 1.53, p = 0.02) as independent predictors of CAE. ROC analysis demonstrated that the NPAR had superior predictive value over CRP (AUC: 0.725 vs. 0.635). Additionally, the NPAR showed a strong correlation with CAE severity based on the Markis classification, with higher NPAR values associated with more advanced disease. Conclusions: The NPAR is an independent predictor of CAE and outperforms CRP in predicting both the presence and severity of the condition. As a cost-effective and accessible biomarker, the NPAR may enhance the clinical assessment of CAE and provide valuable insights into its inflammatory underpinnings. Further prospective studies are warranted to validate these findings and explore the potential of the NPAR in risk stratification and management of CAE patients. Full article

(This article belongs to the Section Clinical Laboratory Medicine)

► Show Figures

Figure 1

13 pages, 2810 KiB

Open AccessArticle

Sudden Unexpected Infant and Perinatal Death: Pathological Findings of the Cardiac Conduction System

by Giulia Ottaviani, Patrizia Leonardi, Massimo Del Fabbro and Simone G. Ramos

Diagnostics 2025, 15(13), 1637; https://doi.org/10.3390/diagnostics15131637 - 27 Jun 2025

Abstract

Objective: Sudden infant death syndrome (SIDS), sudden neonatal unexpected death (SNUD), and sudden intrauterine unexpected death (SIUD) are major unsolved, shocking forms of death that occur frequently and without warning. The body of literature on the anatomo-pathological substrates in the cardiac conduction system [...] Read more.

Objective: Sudden infant death syndrome (SIDS), sudden neonatal unexpected death (SNUD), and sudden intrauterine unexpected death (SIUD) are major unsolved, shocking forms of death that occur frequently and without warning. The body of literature on the anatomo-pathological substrates in the cardiac conduction system of SIDS-SIUD and their possible relationship with risk factors and triggers is fragmentary and scarce. The work aims is to analyze the cardiac conduction system findings collected at the national referral center for SIDS-SIUD. Methods: A total of 123 autopsied cases of SIDS (59.35% males, 40.65% females, mean age ± SD: 103.49 ± 67.17 days), 36 cases of SNUD (61.11% males, 38.89% females, mean age ± SD: 8.4 ± 9.17 days), and 127 cases of SIUD (45.67% males, 54.33% females, mean age ± SD: 36 ± 4.59 gestational weeks) were analyzed. In-depth pathological examinations of the cardiac conduction system were performed on serial sections according to the Lino Rossi Research Center’s protocol. Results: Among the studied cases, the following findings were observed: resorptive degeneration (SIDS: 88.7%, SNUD: 88.88%, SIUD: 56.69%), fetal dispersion (SIDS: 73.17%, SNUD: 91.66%, SIUD: 78.74%), Mahaim fibers (SIDS: 40.65%, SNUD: 44.44%, SIUD: 32.28%), cartilaginous meta-hyperplasia (SIDS: 56.91%, SNUD: 25%, SIUD: 33.07%), septated atrio-ventricular junction (AVJ) (SIDS: 21.14%, SNUD: 33.33%, SIUD: 38.58%), AVJ duplicity (SIDS: 6.5%, SNUD: 11.11%, SIUD: 2.36%), intramural bifurcation (SIDS: 3.25%, SNUD: 2.77%, SIUD: 4.72%). Conclusions: The prevalence of cardiac conduction findings was consistent across the SIDS, SNUD and SIUD groups. These findings provide valuable insights into the pathological characteristics of the cardiac conduction system in SIDS-SIUD that are potential morphological substrates for the development of cardiac arrhythmias. Further investigation and study of the conduction system are needed to understand the underlying mechanisms of these forms of death. Full article

(This article belongs to the Special Issue Autopsy for Medical Diagnostics: 3rd Edition)

► Show Figures

Figure 1

19 pages, 2705 KiB

Open AccessArticle

Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data

by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer and Muhammed Yildirim

Diagnostics 2025, 15(13), 1636; https://doi.org/10.3390/diagnostics15131636 - 26 Jun 2025

Abstract

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not [...] Read more.

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes. Full article

(This article belongs to the Special Issue Deep Learning in Biomedical Image and Signal Processing: Recent Advancements and Applications)

► Show Figures

Figure 1

19 pages, 716 KiB

Open AccessReview

Chatbots in Radiology: Current Applications, Limitations and Future Directions of ChatGPT in Medical Imaging

by Ludovica R. M. Lanzafame, Claudia Gulli, Silvio Mazziotti, Giorgio Ascenti, Michele Gaeta, Thomas J. Vogl, Ibrahim Yel, Vitali Koch, Leon D. Grünewald, Giuseppe Muscogiuri, Christian Booz and Tommaso D’Angelo

Diagnostics 2025, 15(13), 1635; https://doi.org/10.3390/diagnostics15131635 - 26 Jun 2025

Abstract

Artificial intelligence (AI) is reshaping radiological practice, with recent advancements in natural language processing (NLP), large language models (LLMs), and chatbot technologies opening new avenues for clinical integration. These AI-driven conversational agents have demonstrated potential in streamlining patient triage, optimizing imaging protocol selection, [...] Read more.

Artificial intelligence (AI) is reshaping radiological practice, with recent advancements in natural language processing (NLP), large language models (LLMs), and chatbot technologies opening new avenues for clinical integration. These AI-driven conversational agents have demonstrated potential in streamlining patient triage, optimizing imaging protocol selection, supporting image interpretation, automating radiology report generation, and improving communication among radiologists, referring physicians, and patients. Emerging evidence also highlights their role in decision-making, clinical data extraction, and structured reporting. While the clinical adoption of chatbots remains limited by concerns related to data privacy, model robustness, and ethical oversight, ongoing developments and regulatory efforts are paving the way for responsible implementation. This review provides a critical overview of the current and emerging applications of chatbots in radiology, evaluating their capabilities, limitations, and future directions for clinical and research integration. Full article

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

► Show Figures

Figure 1

12 pages, 949 KiB

Open AccessArticle

Diagnostic Value of T2 Mapping in Sacroiliitis Associated with Spondyloarthropathy

by Mustafa Koyun and Kemal Niyazi Arda

Diagnostics 2025, 15(13), 1634; https://doi.org/10.3390/diagnostics15131634 - 26 Jun 2025

Abstract

Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study [...] Read more.

Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study examined a total of 56 participants, comprising 31 SpA patients (n = 31) and 25 healthy controls (n = 25), who underwent sacroiliac joint MRI between August 2018 and August 2020. T2 mapping images were generated using multi-echo turbo spin echo (TSE) sequence, and quantitative T2 relaxation times were measured from bone and cartilage regions. Statistical analysis employed appropriate parametric and non-parametric tests with significance set at p < 0.05. Results: The mean T2 relaxation time measured from the areas with osteitis of SpA patients (100.23 ± 7.41 ms; 95% CI: 97.51–102.95) was significantly higher than that of the control group in normal bone (69.44 ± 4.37 ms; 95% CI: 67.64–71.24), and this difference was found to be statistically significant (p < 0.001). No significant difference was observed between cartilage T2 relaxation times in SpA patients and controls (p > 0.05). Conclusions: T2 mapping serves as a valuable quantitative imaging biomarker for diagnosing sacroiliitis associated with SpA, particularly by detecting bone marrow edema. The technique shows promise for objective disease assessment, though larger studies are needed to establish standardized reference values for T2 relaxation times in osteitis to enhance diagnostic accuracy and facilitate treatment monitoring. Full article

(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)

► Show Figures

Figure 1

14 pages, 1462 KiB

Open AccessArticle

Analysis of Selected Small Proline-Rich Proteins in Tissue Homogenates from Samples of Head and Neck Squamous Cell Carcinoma

by Dariusz Nałęcz, Agata Świętek, Dorota Hudy, Zofia Złotopolska, Jakub Opyrchał, David Aebisher and Joanna Katarzyna Strzelczyk

Diagnostics 2025, 15(13), 1633; https://doi.org/10.3390/diagnostics15131633 - 26 Jun 2025

Abstract

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) ranks sixth in the world in terms of incidence. Small proline-rich proteins (SPRRs) are precursors of the keratinocyte envelope and act as substrates of transglutaminase. A change in SPRR expression is characteristic in a [...] Read more.

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) ranks sixth in the world in terms of incidence. Small proline-rich proteins (SPRRs) are precursors of the keratinocyte envelope and act as substrates of transglutaminase. A change in SPRR expression is characteristic in a few types of cancer. Our aim was to determine the concentration of SPRR1A and SPRR2A in tumours samples obtained from 61 patients with HNSCC (OSCC, OPSCC, LSCC, HPSCC, NCSCC, and SSCC). Also, we aimed to determine the relationship between protein concentration and other clinical and/or demographic variables. Methods: An ELISA test was used to determine the concentrations of SPRR in the tumour tissue homogenates. Results: In margin samples, we found a statistically significant association between SPRR1A levels and nodal status (N) and between SPRR1A levels in tumours and margins with G2 histological grade. When we analysed the effect of tobacco and alcohol habits, we found a statistically significant difference between the SPRR1A and SPRR2A amount in smokers and non-smokers in margin samples. Also, we found a statistically significant difference between the SPRR1A and SPRR2A levels in tumour and margin samples obtained from patients that either abstain and occasionally or regularly consume alcohol. Furthermore, we found in tumour and margin samples from patients with concomitant diseases an association between SPRR1A and SPRR2A levels. Our results showed altered concentrations of SPRR1A at margins, depending on HPV status. Conclusions: These results suggest that differences in SPRR proteins are determined by disease status and unhealthy behaviours, which, in a wider perspective, can influence carcinogenesis. Full article

(This article belongs to the Special Issue Advances in the Diagnosis and Management of Head and Neck Disease)

► Show Figures

Figure 1

12 pages, 2532 KiB

Open AccessArticle

Molecular Identification of Meningitis/Septicemia Due to Streptococcus spp. in Greece (2015–2024)

by Constantinos Karamalis, Athanasia Xirogianni, Stelmos Simantirakis, Marina Delegkou, Anastasia Papandreou and Georgina Tzanakaki

Diagnostics 2025, 15(13), 1632; https://doi.org/10.3390/diagnostics15131632 - 26 Jun 2025

Abstract

Background/Objectives: Meningitis due to the species Streptococcus is a severe central nervous system infection caused by various microorganisms belonging to Streptococcus spp. Its accurate identification is critical for effective clinical management. This study aimed to identify Streptococcus spp. causing meningitis in Greece [...] Read more.

Background/Objectives: Meningitis due to the species Streptococcus is a severe central nervous system infection caused by various microorganisms belonging to Streptococcus spp. Its accurate identification is critical for effective clinical management. This study aimed to identify Streptococcus spp. causing meningitis in Greece over a nine-year period using PCR and sequencing-based methods. Methods: A total of 189 clinical samples, collected between 2015 and 2024 from patients suffering from meningitis and/or septicemia, were analyzed by the use of a combination of multiplex polymerase chain reaction (PCR) assays and tuf gene sequencing for further species identification. Results: Sample analysis identified 70 samples as S. pyogenes (18.52%) (GAS) and S. agalactiae (18.52%) (GBS), while 119 (62.96%) were recorded as non-typable Streptococcus spp. Further analysis using sequencing methods revealed that the most frequent Streptococcus spp. belonged to the mitis group (42.86%) and the pyogenic group (20.17%). A higher prevalence was observed in children aged 0–14 years old and adults over 50 years old. Conclusions: This study highlights the use of molecular diagnostics in identifying other Streptococcus spp., providing insights into age-related susceptibility and epidemiological trends. Future studies are needed to explore the pathogenic role of the identified Lactococcus spp. Full article

(This article belongs to the Special Issue Diagnosis and Management of Meningitis—2nd Edition)

► Show Figures

Figure 1

14 pages, 872 KiB

Open AccessArticle

Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk

by Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa and Federica Pediconi

Diagnostics 2025, 15(13), 1631; https://doi.org/10.3390/diagnostics15131631 - 26 Jun 2025

Abstract

Background: Digital breast tomosynthesis (DBT) is a valuable imaging modality for breast cancer detection; however, its interpretation remains time-consuming and subject to inter-reader variability. This study aimed to develop and evaluate two deep learning (DL) models based on transfer learning for the [...] Read more.

Background: Digital breast tomosynthesis (DBT) is a valuable imaging modality for breast cancer detection; however, its interpretation remains time-consuming and subject to inter-reader variability. This study aimed to develop and evaluate two deep learning (DL) models based on transfer learning for the binary classification of breast lesions (benign vs. malignant) using DBT images to support clinical decision-making and risk stratification. Methods: In this retrospective monocentric study, 184 patients with histologically or clinically confirmed benign (107 cases, 41.8%) or malignant (77 cases, 58.2%) breast lesions were included. Each case underwent DBT with a single lesion manually segmented for radiomic analysis. Two convolutional neural network (CNN) architectures—ResNet50 and DenseNet201—were trained using transfer learning from ImageNet weights. A 10-fold cross-validation strategy with ensemble voting was applied. Model performance was evaluated through ROC–AUC, accuracy, sensitivity, specificity, PPV, and NPV. Results: The ResNet50 model outperformed DenseNet201 across most metrics. On the internal testing set, ResNet50 achieved a ROC–AUC of 63%, accuracy of 60%, sensitivity of 39%, and specificity of 75%. The DenseNet201 model yielded a lower ROC–AUC of 55%, accuracy of 55%, and sensitivity of 24%. Both models demonstrated relatively high specificity, indicating potential utility in ruling out malignancy, though sensitivity remained suboptimal. Conclusions: This study demonstrates the feasibility of using transfer learning-based DL models for lesion classification on DBT. While the overall performance was moderate, the results highlight both the potential and current limitations of AI in breast imaging. Further studies and approaches are warranted to enhance model robustness and clinical applicability. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging—2nd Edition)

Journal Description

Diagnostics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI