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Beyond the Middle Ear: A Thorough Review of Cholesteatoma in the Nasal Cavity and Paranasal Sinuses
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The Clinical Significance and Potential of Complex Diagnosis for a Large Scar Area Following Myocardial Infarction
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The Zucker Diabetic Fatty Rat as a Model for Vascular Changes in Diabetic Kidney Disease: Characterising Hydronephrosis
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The Association of Heart Failure and Liver T1 Mapping in Cardiac Magnetic Resonance Imaging
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Special Considerations in Pediatric Inflammatory Bowel Disease Pathology
Journal Description
Diagnostics
Diagnostics
is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q1 (Medicine, General and Internal) / CiteScore - Q2 (Internal Medicine)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Diagnostics include: LabMed and AI in Medicine.
Impact Factor:
3.3 (2024);
5-Year Impact Factor:
3.3 (2024)
Latest Articles
A Patient Presenting with Joint Deformities and ST-Elevation Myocardial Infarction
Diagnostics 2025, 15(17), 2254; https://doi.org/10.3390/diagnostics15172254 (registering DOI) - 5 Sep 2025
Abstract
A 62-year-old man presented with ST-elevation myocardial infarction and advanced tophaceous gout, despite long-term urate-lowering therapy. His history included chronic kidney disease, hypertension, heart failure, and atrial fibrillation. Examination revealed severe joint deformities with multiple tophi. Coronary angiography showed multivessel disease with critical
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A 62-year-old man presented with ST-elevation myocardial infarction and advanced tophaceous gout, despite long-term urate-lowering therapy. His history included chronic kidney disease, hypertension, heart failure, and atrial fibrillation. Examination revealed severe joint deformities with multiple tophi. Coronary angiography showed multivessel disease with critical right coronary artery stenosis, treated with primary percutaneous coronary intervention. Following a Heart Team consultation, the patient was bridged with cangrelor and underwent urgent hybrid coronary artery bypass grafting and left atrial appendage occlusion. This case highlights the systemic burden of treatment-refractory gout, with progressive cardiovascular and renal complications. Tophaceous gout represents a distinct, high-risk phenotype associated with increased mortality and reduced quality of life. Despite standard therapies, this patient experienced continued disease progression, prompting referral for advanced treatment with pegloticase and canakinumab. Multidisciplinary care and personalized strategies are essential in managing severe, refractory gout with multi-organ involvement.
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(This article belongs to the Special Issue Clinical Diagnosis and Management in Cardiology)
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Open AccessArticle
AI-Based Response Classification After Anti-VEGF Loading in Neovascular Age-Related Macular Degeneration
by
Murat Fırat, İlknur Tuncer Fırat, Ziynet Fadıllıoğlu Üstündağ, Emrah Öztürk and Taner Tuncer
Diagnostics 2025, 15(17), 2253; https://doi.org/10.3390/diagnostics15172253 - 5 Sep 2025
Abstract
Background/Objectives: Wet age-related macular degeneration (AMD) is a progressive retinal disease characterized by macular neovascularization (MNV). Currently, the standard treatment for wet AMD is intravitreal anti-VEGF administration, which aims to control disease activity by suppressing neovascularization. In clinical practice, the decision to
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Background/Objectives: Wet age-related macular degeneration (AMD) is a progressive retinal disease characterized by macular neovascularization (MNV). Currently, the standard treatment for wet AMD is intravitreal anti-VEGF administration, which aims to control disease activity by suppressing neovascularization. In clinical practice, the decision to continue or discontinue treatment is largely based on the presence of fluid on optical coherence tomography (OCT) and changes in visual acuity. However, discrepancies between anatomic and functional responses can occur during these assessments. Methods: This article presents an artificial intelligence (AI)-based classification model developed to objectively assess the response to anti-VEGF treatment in patients with AMD at 3 months. This retrospective study included 120 patients (144 eyes) who received intravitreal bevacizumab treatment. After bevacizumab loading treatment, the presence of subretinal/intraretinal fluid (SRF/IRF) on OCT images and changes in visual acuity (logMAR) were evaluated. Patients were divided into three groups: Class 0, active disease (persistent SRF/IRF); Class 1, good response (no SRF/IRF and ≥0.1 logMAR improvement); and Class 2, limited response (no SRF/IRF but with <0.1 logMAR improvement). Pre-treatment and 3-month post-treatment OCT image pairs were used for training and testing the artificial intelligence model. Based on this grouping, classification was performed with a Siamese neural network (ResNet-18-based) model. Results: The model achieved 95.4% accuracy. The macro precision, macro recall, and macro F1 scores for the classes were 0.948, 0.949, and 0.948, respectively. Layer Class Activation Map (LayerCAM) heat maps and Shapley Additive Explanations (SHAP) overlays confirmed that the model focused on pathology-related regions. Conclusions: In conclusion, the model classifies post-loading response by predicting both anatomic disease activity and visual prognosis from OCT images.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessArticle
The Predictive Value of Clinical Signs to Identify Shock in Critically Ill Patients
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Matthias Noitz, Sabine Preining, Dominik Jenny, Simon Langthaler, Romana Erblich, Thomas Tschoellitsch, Jens Meier and Martin W. Dünser
Diagnostics 2025, 15(17), 2252; https://doi.org/10.3390/diagnostics15172252 - 5 Sep 2025
Abstract
Background/Objectives: Current guidelines recommend the use of clinical signs to diagnose shock and cellular hypoperfusion in critically ill patients. However, these recommendations are based on limited scientific evidence. The objective was to determine the predictive value of clinical signs to identify shock. Methods:
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Background/Objectives: Current guidelines recommend the use of clinical signs to diagnose shock and cellular hypoperfusion in critically ill patients. However, these recommendations are based on limited scientific evidence. The objective was to determine the predictive value of clinical signs to identify shock. Methods: Retrospective cohort study including adult (≥18 years) patients admitted to the critical care resuscitation unit of a tertiary hospital. The primary goal was to determine the predictive value of tachycardia, prolonged capillary refill time (CRT), skin mottling, weak radial pulse, inadequate peripheral perfusion, shock index >0.8, altered mental state, and diaphoresis to identify shock. Two-by-two contingency tables were used for statistical analysis. Results: Three-hundred-seventeen patients (no shock, n = 231; shock, n = 86) were included. As a single clinical sign, skin mottling [sensitivity, 0.38; specificity, 0.92; negative likelihood ratio (LR−), 0.68; positive likelihood ratio (LR+), 4.62], prolonged CRT (sensitivity, 0.44; specificity, 0.89; LR−, 0.62; LR+, 4.17), shock index >0.8 [sensitivity, 0.77; specificity, 0.64; LR−, 0.36; LR+, 2.15], a weak radial pulse [sensitivity, 0.62; specificity, 0.79; LR−, 0.49; LR+, 2.88], and inadequate peripheral perfusion [sensitivity, 0.68; specificity, 0.73; LR−, 0.44; LR+, 2.52] predicted shock. Prolonged CRT, skin mottling, inadequate peripheral perfusion, a weak radial pulse, and a shock index >0.8 predicted shock states with low cardiac output. A shock index >0.8, tachycardia, and a weak radial pulse were predictive of distributive/vasodilatory shock. The accuracy to identify shock were higher if ≥2 clinical signs were present compared to only one. Conclusions: Skin mottling, prolonged CRT, shock index >0.8, weak radial pulse, and inadequate peripheral perfusion can identify patients with shock, particularly shock states with low cardiac output, with high specificity and LR+.
Full article
(This article belongs to the Special Issue Diagnostics in the Emergency and Critical Care Medicine)
Open AccessArticle
Fetal and Neonatal Outcomes in Fetuses with an Estimated Fetal Weight Percentile of 10–20 in the Early Third Trimester: A Retrospective Cohort Study
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Miguel A. Mendez-Piña, Mario I. Lumbreras-Marquez, Sandra Acevedo-Gallegos, Berenice Velazquez-Torres, Maria J. Rodriguez-Sibaja, Dulce M. Camarena-Cabrera and Juan M. Gallardo-Gaona
Diagnostics 2025, 15(17), 2251; https://doi.org/10.3390/diagnostics15172251 - 5 Sep 2025
Abstract
Background: Fetal size is often dichotomized as normal or abnormal using the 10th percentile of estimated fetal weight (EFW) or abdominal circumference as a cutoff. While the risk of adverse perinatal outcomes decreases with increasing fetal weight percentile, no percentile completely eliminates that
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Background: Fetal size is often dichotomized as normal or abnormal using the 10th percentile of estimated fetal weight (EFW) or abdominal circumference as a cutoff. While the risk of adverse perinatal outcomes decreases with increasing fetal weight percentile, no percentile completely eliminates that risk. Objective: The aim of this study was to compare perinatal outcomes between fetuses with an EFW between the 10th and 20th percentiles and those with an EFW between the 20th and 90th percentiles (i.e., >20 and <90) at the beginning of the accelerated growth stage (28.0–30.0 weeks’ gestation). Methods: We conducted a retrospective cohort study of singleton pregnancies managed at a quaternary center in Mexico City (2017–2024). Outcomes were compared based on EFW percentiles at 28.0–30.0 weeks. The primary outcome was adverse neonatal outcome (ANeO), defined as the presence of at least one of the following: umbilical artery pH ≤ 7.1, 5 min Apgar ≤ 7, NICU admission, early neonatal hypoglycemia, non-reassuring fetal status, respiratory distress syndrome, intraventricular hemorrhage, hypoxic–ischemic encephalopathy, or perinatal death. Secondary outcomes included progression to fetal growth restriction (FGR) and low birth weight. Modified Poisson regression was used to estimate adjusted risk ratios (aRRs) with 95% confidence intervals (CIs). Results: Among 650 cases, ANeO occurred in 45.8% of fetuses in the 10th–20th percentile group vs. 29.4% in the 20th–90th percentile group (aRR: 1.51, 95% CI: 1.22–1.86; p < 0.001). FGR and low birth weight were also more frequent in the 10th–20th percentile group (21.1% and 27.6% vs. 6.4% and 5.8%, respectively; p < 0.001). Conclusions: Fetuses between the 10th and 20th percentiles at 28–30 weeks have increased risks of neonatal morbidity, FGR, and low birth weight.
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(This article belongs to the Special Issue Diagnosis and Management of Contemporary Issues in Maternal-Fetal Medicine)
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Open AccessArticle
Temporal Trends and Machine Learning-Based Risk Prediction of Female Infertility: A Cross-Cohort Analysis Using NHANES Data (2015–2023)
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Ismat Ara Begum, Deepak Ghimire and A. S. M. Sanwar Hosen
Diagnostics 2025, 15(17), 2250; https://doi.org/10.3390/diagnostics15172250 - 5 Sep 2025
Abstract
Background: Female infertility represents a significant global public health concern, yet its evolving trends and data-driven risk prediction remain under examined in nationally representative cohorts. This study investigates temporal changes in infertility prevalence and evaluates Machine Learning (ML) models for infertility risk prediction
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Background: Female infertility represents a significant global public health concern, yet its evolving trends and data-driven risk prediction remain under examined in nationally representative cohorts. This study investigates temporal changes in infertility prevalence and evaluates Machine Learning (ML) models for infertility risk prediction using harmonized clinical features from NHANES cycles (2015, 2016, 2017, 2018, 2021, 2022, and 2023). Methods: Women aged 19 to 45 with complete data on infertility-related variables (including reproductive history, menstrual irregularity, Pelvic Infection Disease (PID), hysterectomy, and bilateral oophorectomy) were analyzed. Descriptive statistics and cohort comparisons employed ANOVA and Chi-square tests, while multivariate Logistic Regression (LR) estimated Adjusted Odds Ratios (OR) and informed feature importance. Predictive models (LR, Random Forest, XGBoost, Naive Bayes, SVM, and a Stacking Classifier ensemble) were trained and tuned via GridSearchCV with five-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, specificity, and AUC-ROC. Results: We observed a notable increase in infertility prevalence from 14.8% in 2017–2018 to 27.8% in 2021–2023, suggesting potential post-pandemic impacts on reproductive health. In multivariate analysis, prior childbirth emerged as the strongest protective factor (Adjusted OR ), while menstrual irregularity showed a significant positive association with infertility (OR , 95% CI to , ). Unexpectedly, PID, hysterectomy, and bilateral oophorectomy were not significantly associated with infertility after adjustment ( ), which may partly reflect the inherent definition of self-reported infertility used in this study. All six ML models demonstrated excellent and comparable predictive ability (AUC ), reinforcing the effectiveness of even a minimal common predictor set for infertility risk stratification. Conclusions: The rising prevalence of self-reported infertility among U.S. women underscores emerging public health challenges. Despite relying on a streamlined feature set, interpretable and ensemble ML models successfully predicted infertility risk, showcasing their potential applicability in broader surveillance and personalized care strategies. Future models should integrate additional sociodemographic and behavioral factors to enhance precision and support tailored interventions.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessArticle
Simultaneous Detection and Differentiation of SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Viruses in Respiratory Specimens Using the VitaSIRO solo™ SARS-CoV-2/Flu/RSV Assay
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Ralph-Sydney Mboumba Bouassa, Sarah Lukumbisa and Laurent Bélec
Diagnostics 2025, 15(17), 2249; https://doi.org/10.3390/diagnostics15172249 - 5 Sep 2025
Abstract
Background/Objectives: The concurrent circulation of SARS-CoV-2 with influenza A and B viruses and respiratory syncytial virus (RSV) represents a new diagnostic challenge in the post-COVID-19 area, especially considering that these infections have overlapping clinical presentations but different approaches to treatment and management. Multiplexed
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Background/Objectives: The concurrent circulation of SARS-CoV-2 with influenza A and B viruses and respiratory syncytial virus (RSV) represents a new diagnostic challenge in the post-COVID-19 area, especially considering that these infections have overlapping clinical presentations but different approaches to treatment and management. Multiplexed molecular testing on point-of-care platforms that focus on the simultaneous detection of multiple respiratory viruses in a single tube constitutes a useful approach for diagnosis of respiratory infections in decentralized clinical settings. This study evaluated the analytical performances of the VitaSIRO solo™ SARS-CoV-2/Flu/RSV Assay performed on the VitaSIRO solo™ Instrument (Credo Diagnostics Biomedical Pte. Ltd., Singapore, Republic of Singapore). Methods: With a view to accreditation, the criteria of the 2022-revised EN ISO 15189:2022 norma were applied for the retrospective on-site verification of method using anonymized respiratory specimens collected during the last 2024–2025 autumn–winter season in France. Results: Usability and satisfaction were comparable to current reference point-of-care platforms, such as the Cepheid GeneXpert® Xpress System (Cepheid Diagnostics, Sunnyvale, CA, USA). Repeatability and reproducibility (2.34–4.49% and 2.78–5.71%, respectively) demonstrated a high level of precision. The platform exhibited a low invalid rate (2.9%), with most resolving on retesting. Analytical performance on 301 clinical samples showed high overall sensitivities: 94.8% for SARS-CoV-2 (Ct ≤ 33), 95.8% for influenza A and B viruses, 95.2% for RSV, and 95.4% for all viruses. Specificities were consistently high (99.2–100.0%). False negatives (2.6%) were predominantly associated with high Ct values. Agreement with the comparator reference NeuMoDx™ Flu A-B/RSV/SARS-CoV-2 Vantage Assay (Qiagen GmbH, Hilden, Germany) was almost perfect (Cohen’s κ 0.939–0.974), and a total of 91.1%, 94.8%, and 100.0% of Ct values were within the 95% limits of agreement for the detection of SARS-CoV-2, influenza A and B viruses, and RSV, respectively, by Bland–Altman analyses. Passing–Bablok regression analyses demonstrated good Ct values correlation between VitaSIRO solo™ and NeuMoDx™ assays, with a slight, non-significant, positive bias for the VitaSIRO solo™ assay (mean absolute bias +0.509 to +0.898). Conclusions: These findings support VitaSIRO solo™ Instrument as a user-friendly and reliable point-of-care platform for the rapid detection and differentiation of SARS-CoV-2, influenza A and B viruses, and RSV responding to the EN ISO 15189:2022 criteria for accreditation to be implemented in hospital or decentralized settings.
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(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
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Open AccessArticle
Artificial Intelligence-Based MRI Segmentation for the Differential Diagnosis of Single Brain Metastasis and Glioblastoma
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Daniela Pomohaci, Emilia-Adriana Marciuc, Bogdan-Ionuț Dobrovăț, Mihaela-Roxana Popescu, Ana-Cristina Istrate, Oriana-Maria Onicescu (Oniciuc), Sabina-Ioana Chirica, Costin Chirica and Danisia Haba
Diagnostics 2025, 15(17), 2248; https://doi.org/10.3390/diagnostics15172248 - 5 Sep 2025
Abstract
Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination
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Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination of conventional and advanced MRI techniques can overcome this issue. Methods: We retrospectively selected 78 patients with confirmed GBM (39) and single BM (39), with conventional MRI investigations, consisting of T2W FLAIR and CE T1W acquisitions. The MRI images (DICOM) were evaluated by an AI segmentation tool, comparatively evaluating tumor heterogeneity and peripheral edema. Results: We found that GBMs are less edematous than BMs (p = 0.04) but have more internal necrosis (p = 0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the highest grade of edema (p = 0.01). Compared with the ellipsoidal method of volume calculation, the AI machine obtained greater values when measuring lesions of the occipital and temporal lobes (p = 0.01). Conclusions: Although extremely useful in radiomics analysis, automated segmentation applied alone could effectively differentiate GBM and BM on a conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheral edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessArticle
Stratifying Treatment-Resistant Monosymptomatic Nocturnal Enuresis: Identifying the Subgroup Most Responsive to Biofeedback Therapy
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Emre Kandemir, Ali Sezer and Mehmet Sarikaya
Diagnostics 2025, 15(17), 2247; https://doi.org/10.3390/diagnostics15172247 - 5 Sep 2025
Abstract
Background/Objectives: A subset of children with monosymptomatic nocturnal enuresis (MNE) remains unresponsive to standard treatments such as desmopressin and alarm therapy. This study aimed to identify clinical predictors of response to biofeedback therapy in treatment-resistant MNE and to evaluate the role of
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Background/Objectives: A subset of children with monosymptomatic nocturnal enuresis (MNE) remains unresponsive to standard treatments such as desmopressin and alarm therapy. This study aimed to identify clinical predictors of response to biofeedback therapy in treatment-resistant MNE and to evaluate the role of bladder capacity as a stratification parameter. Methods: In this prospective study, 89 children with treatment-resistant MNE underwent six weekly sessions of biofeedback therapy involving visual pelvic floor feedback. Based on treatment outcomes, patients were classified as complete responders or partial/non-responders. Clinical characteristics including age-adjusted maximal voided volume (MVV), nocturnal polyuria, and wetting frequency were compared. Results: Patients with a complete response had significantly lower baseline MVV and age-adjusted MVV (p < 0.001). Nocturnal overactivity was more common among responders (60.6% vs. 33.9%, p = 0.017), whereas nocturnal polyuria was more frequent in non-responders (p = 0.027). Age-adjusted MVV emerged as the only independent predictor of treatment success in multivariate analysis (p = 0.045), with ROC analysis confirming its predictive value (AUC = 0.767, 95% CI: 0.667–0.866). Conclusions: These findings suggest that reduced bladder capacity and frequent night-time wetting may help identify patients who are more likely to benefit from biofeedback therapy. Bladder capacity assessment may thus serve as a useful tool in tailoring management strategies for refractory MNE.
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(This article belongs to the Special Issue Clinical Diagnosis and Management in Pediatric Surgery)
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Open AccessReview
Diagnostic Innovations to Combat Antibiotic Resistance in Critical Care: Tools for Targeted Therapy and Stewardship
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Ahmed D. Alatawi, Helal F. Hetta, Mostafa A. Sayed Ali, Yasmin N. Ramadan, Amirah B. Alaqyli, Wareef K. Alansari, Nada H. Aldhaheri, Talidah A. Bin Selim, Shahad A. Merdad, Maram O. Alharbi, Wejdan Alhumaidi Hmdan Alatawi and Abdelazeem M. Algammal
Diagnostics 2025, 15(17), 2244; https://doi.org/10.3390/diagnostics15172244 - 5 Sep 2025
Abstract
Antibiotic resistance is a growing global health threat, with critical care settings representing one of the most vulnerable arenas due to the high burden of infection and frequent empirical antibiotic use. Rapid and precise diagnosis of infectious pathogens is crucial for initiating appropriate
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Antibiotic resistance is a growing global health threat, with critical care settings representing one of the most vulnerable arenas due to the high burden of infection and frequent empirical antibiotic use. Rapid and precise diagnosis of infectious pathogens is crucial for initiating appropriate therapy, minimizing unnecessary antimicrobial exposure, and supporting effective stewardship programs. This review explores how innovative diagnostic technologies are reshaping infection management and antimicrobial stewardship in critical care. We examine the clinical utility of molecular assays, multiplex PCR, MALDI-TOF mass spectrometry, metagenomic sequencing, point-of-care (POC) diagnostics, and emerging tools like biosensors and AI-powered predictive models. These platforms enable earlier pathogen identification and resistance profiling, facilitating timely and targeted therapy while minimizing unnecessary broad-spectrum antibiotic use. By integrating diagnostics into stewardship frameworks, clinicians can optimize antimicrobial regimens, improve patient outcomes, and reduce resistance selection pressure. Despite their promise, adoption is challenged by cost, infrastructure, interpretation complexity, and inequitable access, particularly in low-resource settings. Future perspectives emphasize the need for scalable, AI-enhanced, and globally accessible diagnostic solutions. In bridging innovation with clinical application, diagnostic advancements can serve as pivotal tools in the global effort to curb antimicrobial resistance in critical care environments.
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(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
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Open AccessInteresting Images
Proximal Epithelioid Sarcoma Mimicking Inguinal Inflammation
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Tomonori Kawasaki, Takuya Watanabe, Satoshi Kanno, Tomoaki Torigoe, Kojiro Onohara, Masanori Wako, Tetsuhiro Hagino and Jiro Ichikawa
Diagnostics 2025, 15(17), 2246; https://doi.org/10.3390/diagnostics15172246 - 4 Sep 2025
Abstract
Epithelioid sarcoma (ES) is an extremely rare sarcoma categorized into classic and proximal types. Proximal ES is characterized by its occurrence in older individuals, proximal locations, deep tissue involvement, and a tendency to be larger in size. We present a case of an
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Epithelioid sarcoma (ES) is an extremely rare sarcoma categorized into classic and proximal types. Proximal ES is characterized by its occurrence in older individuals, proximal locations, deep tissue involvement, and a tendency to be larger in size. We present a case of an extremely small proximal ES occurring in the inguinal region, which posed significant diagnostic challenges. Clinically, the lesion presented as a painful mass, and magnetic resonance imaging findings suggested lymphadenitis or other inflammatory lesions due to its small size and internal signal patterns. Despite being monitored, the mass showed progression, prompting an incisional biopsy that raised suspicion for ES. Positron emission tomography–computed tomography confirmed the absence of metastases, leading to wide excision. Pathological examination of the excised specimen confirmed proximal ES with negative margins. In this case, characteristic features of proximal ES were scarcely observed, and imaging findings were not distinctive, likely due to the small size of the lesion. Furthermore, the broad differential diagnoses for inguinal masses necessitate careful attention during diagnosis. For sarcomas and tumors in general, reliance solely on clinical and imaging findings can lead to diagnostic pitfalls, emphasizing the importance of active pathological evaluation through biopsy.
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(This article belongs to the Special Issue Advanced Musculoskeletal Imaging in Clinical Diagnostics)
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Open AccessArticle
Conventional Diagnostic Approaches to Dermatophytosis: Insights from a Three-Year Survey at a Public Dermatology Institute in Italy (2019–2021)
by
Eugenia Giuliani, Maria Gabriella Donà, Amalia Giglio, Elva Abril, Francesca Sperati, Fulvia Pimpinelli and Alessandra Latini
Diagnostics 2025, 15(17), 2245; https://doi.org/10.3390/diagnostics15172245 - 4 Sep 2025
Abstract
Background/Objectives: Dermatophytosis is a widespread superficial fungal infection affecting skin, hair, and nails. Its diagnosis is often based on conventional methods such as microscopy and fungal culture. Laboratory confirmation is essential for guiding appropriate treatment and preventing the misuse of antifungal agents,
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Background/Objectives: Dermatophytosis is a widespread superficial fungal infection affecting skin, hair, and nails. Its diagnosis is often based on conventional methods such as microscopy and fungal culture. Laboratory confirmation is essential for guiding appropriate treatment and preventing the misuse of antifungal agents, which can contribute to the emergence of antifungal resistance. We retrospectively assessed the burden and species distribution of dermatophytosis in individuals attending a public dermatology institute in Italy over a 3-year period (2019–2021). Methods: We analyzed 3208 samples from 3037 individuals with clinical suspicion of superficial mycosis. All samples underwent direct microscopic examination and fungal culture. Data were stratified by demographics, body site, and fungal species. Agreement between diagnostic methods was assessed using raw concordance and Cohen’s Kappa statistic. Results: Dermatophytes were confirmed in 667 samples (20.8%). Buttocks and genitals showed the highest positivity rates (37.5% and 36.4%, respectively). T. rubrum (56.8%) and T. mentagrophytes (30.7%) were the predominant species among the dermatophyte-positive specimens. Agreement between microscopy and culture was good (raw concordance: 91.6%, Cohen’s Kappa: 0.77, 95% CI: 0.74–0.79). Younger age and male gender were significantly associated with dermatophyte positivity. Conclusions: Our data provide updated epidemiological insights into dermatophytosis in Italy and support appropriate antifungal stewardship. Laboratory confirmation remains essential for an accurate diagnosis and species identification, thus avoiding other non-dermatophytic or non-infectious conditions being treated as dermatophytosis.
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(This article belongs to the Special Issue Inflammatory and Infectious Skin Diseases: From Diagnostics to Management)
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Open AccessReview
Diagnostics, Efficacy, and Safety of Immunomodulatory and Anti-Fibrotic Treatment for Interstitial Lung Disease Associated with Systemic Scleroderma (SSc-ILD)
by
Dawid Piecuch, Edyta Hanczyk, Katarzyna Zemsta, Michał Zwoliński, Szymon Kopciał and Joanna Jońska
Diagnostics 2025, 15(17), 2243; https://doi.org/10.3390/diagnostics15172243 - 4 Sep 2025
Abstract
Systemic scleroderma (SSc) is an autoimmune disease characterized by excessive collagen production and progressive fibrosis. As the disease advances, vascular injury leads to fibrosis of the skin and internal organs, among which interstitial lung disease (ILD) carries the worst prognosis. Recent advances in
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Systemic scleroderma (SSc) is an autoimmune disease characterized by excessive collagen production and progressive fibrosis. As the disease advances, vascular injury leads to fibrosis of the skin and internal organs, among which interstitial lung disease (ILD) carries the worst prognosis. Recent advances in biomarkers, imaging techniques, and innovative therapies offer hope for improving outcomes and quality of life in patients with SSc and ILD. To evaluate the usefulness of disease biomarkers and the efficacy and safety of immunomodulatory therapies in SSc-associated ILD (SSc-ILD), a literature review was conducted using the PubMed database for studies published mainly over the last 5 years. After applying inclusion criteria, 53 clinical studies were analyzed. Treating SSc-ILD remains challenging, with therapeutic strategies aiming to suppress inflammation and limit fibrosis progression. Clinical studies have demonstrated moderate to good efficacy of immunosuppressants such as cyclophosphamide (CYC) and mycophenolate mofetil (MMF), showing improvements in lung function parameters, such as forced vital capacity (FVC), and slowing disease progression. Additionally, biological agents such as nintedanib and tocilizumab have shown promising results—nintedanib in reducing the annual rate of FVC decline and tocilizumab in decreasing inflammatory biomarkers and stabilizing pulmonary function. However, despite these therapeutic advances, many studies had small sample sizes, heterogeneous patient populations, and varying inclusion criteria. Given the challenges in diagnostics and the critical need to evaluate the efficacy alongside the safety of immunomodulatory and anti-fibrotic therapies in systemic sclerosis-associated interstitial lung disease (SSc-ILD), there remains a strong demand for large, well-designed, multicenter trials with clearly defined patient cohorts to reliably assess the long-term outcomes of agents such as tocilizumab and nintedanib.
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(This article belongs to the Special Issue Diagnostic Imaging of Autoimmune Diseases)
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Open AccessCase Report
Beyond the Urogenital Tract, the Role of Ureaplasma parvum in Invasive Infection in Adults: A Case Series and Literature Review
by
Linhui Hu, Xiangyan Li, Dan Liu, Jie Yao, Xueying Li and Yan Wang
Diagnostics 2025, 15(17), 2242; https://doi.org/10.3390/diagnostics15172242 - 4 Sep 2025
Abstract
Background/Objectives: Ureaplasma parvum (Up) is an opportunistic pathogen associated with urogenital tract infections, pregnancy complications, and reproductive system diseases. Advances in molecular diagnostics have expanded its pathogenic spectrum to include invasive conditions such as arthritis, meningitis, and pneumonia. However, the
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Background/Objectives: Ureaplasma parvum (Up) is an opportunistic pathogen associated with urogenital tract infections, pregnancy complications, and reproductive system diseases. Advances in molecular diagnostics have expanded its pathogenic spectrum to include invasive conditions such as arthritis, meningitis, and pneumonia. However, the pathogenic significance of Up remains controversial. Methods: This study retrospectively analyzed nine adult cases of Up detected by metagenomic next-generation sequencing (mNGS) between 2023 and 2024. Results: Patients, aged 21 to 70 years, predominantly had underlying immunosuppressive conditions (66.7%). Infections involved the urinary system (44.4%), respiratory system (33.3%), and peritoneal cavity (22.2%). Symptomatic relief was achieved in five cases following treatment with tetracyclines, quinolones or tigecycline. Conclusions: These findings highlight Up as a potential causative agent of invasive infections, particularly in immunocompromised patients. Up has potential pathogenic significance, whether it is detected as a single pathogen or as a coexisting pathogen.
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(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
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Open AccessInteresting Images
An Uncommon Cause of Angina
by
David S. Majdalany, Elaina A. Blickenstaff, Francois Marcotte and Jason H. Anderson
Diagnostics 2025, 15(17), 2241; https://doi.org/10.3390/diagnostics15172241 - 4 Sep 2025
Abstract
Coronary anomalies, although rare, should be considered when young patients present with angina. Clinical suspicion and multi-modality imaging including coronary angiography and tomographic imaging should be pursued for symptomatic patients such as the one we are presenting with anomalous right coronary artery from
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Coronary anomalies, although rare, should be considered when young patients present with angina. Clinical suspicion and multi-modality imaging including coronary angiography and tomographic imaging should be pursued for symptomatic patients such as the one we are presenting with anomalous right coronary artery from the pulmonary artery. She was promptly referred for surgical intervention with re-implantation of the right coronary artery onto the aorta.
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(This article belongs to the Special Issue Advanced Diagnostic Approaches in Cardiovascular Diseases: From Imaging to Biomarkers)
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Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs
by
Mehmet Birol Özel, Sultan Büşra Ay Kartbak and Muhammet Çakmak
Diagnostics 2025, 15(17), 2240; https://doi.org/10.3390/diagnostics15172240 - 3 Sep 2025
Abstract
Background/Objectives: Vertical growth pattern significantly influences facial aesthetics and treatment choices. Lateral cephalograms are routinely used for the evaluation of vertical jaw relationships in orthodontic diagnosis. The aim of this study was to evaluate the performance of deep learning algorithms in classifying
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Background/Objectives: Vertical growth pattern significantly influences facial aesthetics and treatment choices. Lateral cephalograms are routinely used for the evaluation of vertical jaw relationships in orthodontic diagnosis. The aim of this study was to evaluate the performance of deep learning algorithms in classifying cephalometric radiographs according to vertical skeletal growth patterns without the need for anatomical landmark identification. Methods: This study was carried out on lateral cephalometric radiographs of 1050 patients. Cephalometric radiographs were divided into 3 subgroups based on FMA, SN-GoGn, and Cant of Occlusal Plane angles. Six deep learning models (ResNet101, DenseNet 201, EfficientNet B0, EfficientNet V2 B0, ConvNetBase, and a hybrid model) were employed for the classification of the dataset. The performances of the well-known deep learning models and the hybrid model were compared for accuracy, precision, F1-Score, mean absolute error, Cohen’s Kappa, and Grad-CAM metrics. Results: The highest accuracy rates were achieved by the Hybrid Model with 86.67% for FMA groups, 87.29% for SN-GoGn groups, and 82.71% for Cant of Occlusal Plane groups. The lowest accuracy rates were achieved by ConvNet with 79.58% for FMA groups, 65% for SN-GoGn, and 70.21% for Cant of Occlusal Plane groups. Conclusions: The six deep learning algorithms employed demonstrated classification success rates ranging from 65% to 87.29%. The highest classification accuracy was observed in the FMA angle, while the lowest accuracy was recorded for the Cant of the Occlusal Plane angle. The proposed DL algorithms showed potential for direct skeletal orthodontic diagnosis without the need for cephalometric landmark detection steps.
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(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine)
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A Virtual Reality-Based Multimodal Approach to Diagnosing Panic Disorder and Agoraphobia Using Physiological Measures: A Machine Learning Study
by
Han Wool Jung, Hyun Park, Seon-Woo Lee, Ki Won Jang, Sangkyu Nam, Jong Sub Lee, Moo Eob Ahn, Sang-Kyu Lee, Yeo Jin Kim and Daeyoung Roh
Diagnostics 2025, 15(17), 2239; https://doi.org/10.3390/diagnostics15172239 - 3 Sep 2025
Abstract
Objectives: Virtual reality (VR) has emerged as a promising tool for assessing anxiety-related disorders through immersive exposure and physiological monitoring. This study aimed to evaluate whether multimodal data, including heart rate variability (HRV), skin conductance response (SCR), and self-reported anxiety, collected during
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Objectives: Virtual reality (VR) has emerged as a promising tool for assessing anxiety-related disorders through immersive exposure and physiological monitoring. This study aimed to evaluate whether multimodal data, including heart rate variability (HRV), skin conductance response (SCR), and self-reported anxiety, collected during VR exposure could classify patients with panic disorder and agoraphobia using machine learning models. Methods: Seventy-six participants (38 patients with panic disorder and agoraphobia, 38 healthy controls) completed 295 total VR exposure sessions. Each session involved two road and two supermarket scenarios designed to induce anxiety. Inside the sessions, self-reported anxiety was measured along with physiological signals recorded by photoplethysmography and SCR sensors. HRV measures of heart rate, standard deviation of normal-to-normal intervals, and low-frequency to high-frequency ratio were extracted along with SCR peak frequency and average amplitude. These features were analyzed using Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (k-NN), Logistic Ridge Regression (LRR), C-Support Vector Machine (SVC), Random Forest (RF), and Stochastic Gradient Boosting (SGB) classifiers. Results: The best model achieved an accuracy of 0.83. Most models showed specificity and precision ≥0.80, while sensitivity varied across models, with several reaching ≥0.82. Performance was stable across major hyperparameters, VR-stimulus settings, and medication status. The patients reported higher subjective anxiety but exhibited blunted physiological responses, particularly in SCR amplitude. Self-reported anxiety demonstrated higher feature importance scores compared to other physiological properties. Conclusion: VR exposure with self-reported anxiety and physiological measures may serve as a feasible diagnostic aid for panic disorder and agoraphobia. Further refinement is needed to improve sensitivity and clinical applicability.
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(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
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Pulmonary Fungal Infections in a Tertiary Cancer Center: A Morphological Correlation of 160 Cases with CT and PET Imaging
by
Sebastian Lyos, Mylene T. Truong and Cesar A. Moran
Diagnostics 2025, 15(17), 2238; https://doi.org/10.3390/diagnostics15172238 - 3 Sep 2025
Abstract
Background: Pulmonary fungal infections can mimic malignancies, especially in patients with a prior cancer diagnosis. This study presents 160 patients who were suspected to have malignancies but were diagnosed with fungal infections. Methods: Clinical, radiological, and histopathological features were recorded for all 160
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Background: Pulmonary fungal infections can mimic malignancies, especially in patients with a prior cancer diagnosis. This study presents 160 patients who were suspected to have malignancies but were diagnosed with fungal infections. Methods: Clinical, radiological, and histopathological features were recorded for all 160 patients. The patients included 61 women and 99 men, aged between 23 and 78 years (median age: 61 years). Diagnostic imaging identified either single or multiple pulmonary nodules. Tissue diagnosis was obtained in all cases, identifying various etiological agents, with Histoplasma, Cryptococcus, and Aspergillus being the top three infections. Results: Out of the 160 patients, 61 (38.1%) had a prior history of malignancy, and 29 (18.1%) had ongoing evidence of malignancy. Ninety-nine patients had no history of prior malignancy but presented with abnormal diagnostic imaging findings. The presence of single or multiple lesions in the lung, especially in patients with a history of malignancy, posed a diagnostic challenge, often raising the possibility of metastatic disease or primary lung malignancy. Conclusions: Patients with single or multiple pulmonary nodules, particularly those with a history of malignancy, should undergo tissue diagnosis to accurately define the process. This comprehensive assessment is crucial to determine whether the nodules are due to an infectious process or malignancy.
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(This article belongs to the Special Issue Lung Imaging: Highlights of Recent Research and Clinical Applications)
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Prognostic Value of the CALLY Index in Hypopharyngeal Cancer Treated with Definitive Chemoradiotherapy: A Retrospective Cohort Study
by
Hasan Oguz Cetinayak, Barbaros Aydin, Volkan Semiz, Ece Atac Kutlu, Umut Basan and Rahmi Atıl Aksoy
Diagnostics 2025, 15(17), 2237; https://doi.org/10.3390/diagnostics15172237 - 3 Sep 2025
Abstract
Background: The hypopharyngeal region is among the most aggressive sites of head and neck squamous cell carcinoma, often presenting at an advanced stage with poor survival outcomes. However, there are only a limited number of biomarkers available to predict the prognosis of this
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Background: The hypopharyngeal region is among the most aggressive sites of head and neck squamous cell carcinoma, often presenting at an advanced stage with poor survival outcomes. However, there are only a limited number of biomarkers available to predict the prognosis of this aggressive disease. Recent interest has focused on immunonutritional biomarkers that may improve prognostication. The C-reactive protein–albumin–lymphocyte (CALLY) index has emerged as a composite biomarker integrating systemic inflammation, nutritional status, and immune competence. However, its clinical relevance in hypopharyngeal cancer has not been established. Methods: This retrospective, single-center study included patients with histologically confirmed hypopharyngeal squamous cell carcinoma treated with definitive chemoradiotherapy between 2010 and 2024. Patients were excluded from the study if they had incomplete laboratory data, had a concomitant malignancy, were undergoing induction chemotherapy, or had diseases affecting inflammatory and immunological markers. The CALLY index was calculated using pre-treatment laboratory values. Receiver operating characteristic (ROC) analysis determined the optimal cut-off value for overall survival (OS). Kaplan–Meier survival estimates and Cox regression analyses were used to assess associations between the CALLY index and progression-free survival (PFS), local recurrence-free survival (LRFS), and OS. Results: A total of 71 patients were included. The optimal CALLY cut-off was 1.47 (AUC = 0.70, p = 0.006). Patients with a CALLY index ≥ 1.47 had significantly improved median PFS (37 vs. 9 months, p = 0.003), LRFS (39 vs. 9 months, p = 0.002), and OS (61 vs. 11 months, p = 0.002). In multivariate analysis, the CALLY index and T stage remained independent prognostic factors of all three survival outcomes. Conclusions: The pretreatment CALLY index is a practical, accessible biomarker that independently predicts survival in hypopharyngeal cancer. Its integration into clinical practice may enhance risk stratification and guide individualized management strategies.
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(This article belongs to the Special Issue Advances in the Diagnosis and Management of Head and Neck Disease)
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A Comparison of Three Perfusion Algorithms in Patients at Risk of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
by
Lea Katharina Falter, Dirk Halama, Cordula Scherlach, Felix Arlt, Kristin Starke, Karl-Titus Hoffmann and Cindy Richter
Diagnostics 2025, 15(17), 2236; https://doi.org/10.3390/diagnostics15172236 - 3 Sep 2025
Abstract
Background/Objectives: Delayed cerebral ischemia (DCI) after an aneurysmal subarachnoid hemorrhage (aSAH) often presents with bilateral vasospasm and cortical spreading depolarizations. Computer tomography perfusion (CTP) is the prevailing screening method for detecting early changes in the cerebral blood flow. Commonly used CTP thresholds
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Background/Objectives: Delayed cerebral ischemia (DCI) after an aneurysmal subarachnoid hemorrhage (aSAH) often presents with bilateral vasospasm and cortical spreading depolarizations. Computer tomography perfusion (CTP) is the prevailing screening method for detecting early changes in the cerebral blood flow. Commonly used CTP thresholds include an rCBF < 30% for the core volume and a Tmax > 6 s for hypoperfused tissue detection in acute ischemic stroke. These stroke algorithm computing thresholds compared to the contralateral hemisphere may or may not apply to detect tissue at risk of DCI. We aimed to quantify the volumetric agreement of three different stroke algorithms compared to the final infarct volumes as the standard. Methods: Furthermore, 123 CTP datasets of 75 patients with aSAH suspicious of DCI were processed using Intellispace Portal (ISP), Cercare Threshold, and Cercare Artificial Intelligence (AI) to calculate the tissue-at-risk (hypoperfused) and non-viable tissue (core) volumes. CT infarct volumes in plain CTs were segmented in the follow-up study by using a 3D slicer. Results: The calculated core volumes corresponded best to the final infarct volumes if DCI-related treatment was performed subsequently. Additional postprocessing improved the calculation of core volumes but overestimated the tissue at risk of hypoperfusion in DCI. Whereas the accuracy of tissue-at-risk prediction accelerated without treatment, underlining the importance of intra-arterial spasmolysis and induced hypertension in the prevention of DCI. Conclusions: Cercare AI and ISP revealed a sensitivity of 100% each, with a serious low specificity of <5% that was independent of treatment. Overall, the Cercare Threshold, applying the commonly used stroke thresholds, performed the best in predicting tissue at risk of hypoperfusion in DCI.
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(This article belongs to the Special Issue Optimization of Clinical Imaging: From Diagnosis to Prognosis)
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Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning
by
Tuğçe Taşar Yıldırım, Orhan Yaman, İrfan Kılıç, Beyda Taşar, Esra Suay Timurkaan and Nesibe Aydoğdu
Diagnostics 2025, 15(17), 2235; https://doi.org/10.3390/diagnostics15172235 - 3 Sep 2025
Abstract
Background/Objectives: In this study, a vision transformer (ViT) based ensemble architecture was developed for the classification of normal, benign, and malignant diseases from breast ultrasound images. The breast ultrasound images (BUSI) dataset was used for the implementation of the proposed method. This
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Background/Objectives: In this study, a vision transformer (ViT) based ensemble architecture was developed for the classification of normal, benign, and malignant diseases from breast ultrasound images. The breast ultrasound images (BUSI) dataset was used for the implementation of the proposed method. This dataset includes 133 normal, 437 benign, and 210 malignant ultrasound images. Methods: ROI segmentation and image preprocessing were applied to the dataset to select only the tumor region and use it in the model. Thus, a better performance was achieved using only the lesion regions. Image augmentation was performed using the Albumentations library to increase the number of images. Feature extraction was performed on the obtained images using three ViT-based models (ViT-Base, DeiT, ViT-Small). The purpose of using three different models is to achieve high accuracy. The extracted features were classified using a multilayer perceptron (MLP). Training was performed using 10-fold stratified cross-validation. Results: The purpose of stratified cross-validation is to include a certain number of images from all three classes in each cross-validation proposed model provided 96.2% precision and 86.3% recall for the benign class and 92.9% recall and 76.4% precision for the malignant class. The normal class achieved 100% success. The area under the curve (AUC) values were 0.97, 0.96, and 1.00 for benign and malignant tumors, respectively, and 1.00 for normal tumors. Conclusions: The ROI-based ViT + MLP + Ensemble architecture provided higher accuracy and explainability compared to traditional convolutional neural network (CNN) based methods in medical image classification. It demonstrated a stable success, especially in minority classes, and presented a potential, reliable, and flexible solution in clinical decision support systems.
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(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence, Third Edition)
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