Dermatology

Signal Mining and Analysis of Adverse Events of Isotretinoin: 20-year real-world pharmacovigilance analysis based on the FAERS database

To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (FDA) AE Reporting System (FAERS), aiming to provide references for clinical safety and further research. AE reports from the first quarter of 2004 to the third quarter of 2024 were extracted from the FAERS database. Four signal detection methods were ...

Integrative Approaches for Skin Cancer Detection and Classification : A Dual modal Analysis

The early detection of skin cancer is of critical importance, as it can lead to in fatal outcomes if left unaddressed. Given the limited accessibility of dermatological expertise to the general population, it becomes imperative to devise an economical, efficacious, and precise methodology capable of efficient&reliable diagnosis of melanoma and other forms of skin cancer. A data-driven paradigm eme...

Independent contributions of language activations in left and right temporal cortex to aphasia outcomes after stroke

Recovery from aphasia after stroke is thought to depend on functional reorganization of language processing in surviving brain regions. Many studies h...

Deep Learning for Breast Mass Discrimination: Integration of B-Mode Ultrasound & Nakagami Imaging with Automatic Lesion Segmentation

This study aims to enhance breast cancer diagnosis by developing an automated deep learning framework for real-time, quantitative ultrasound imaging. ...

RAX-NET: Residual Attention Xception Network for Brain Ischemic Stroke Segmentation in T1-Weighted MRI

Ischemic stroke, caused by arterial occlusion, leads to hypoxia and cellular necrosis. Rapid and accurate delineation of ischemic lesions is essential...

A Hybrid Deep Learning Ensemble for Accurate Skin Cancer Classification

Skin cancer is one of the most common types of cancer worldwide, and early detection is crucial for improving patient survival rates. In this study, w...

Body composition and melanoma incidence risk: insights from a longitudinal lung cancer screening cohort

This study explored the association between low-dose computed tomography (LDCT)-derived body composition and melanoma incidence risk. LDCT scans from ...

Multiregional CT Features Improve Prediction of Immunotherapy Response in Advanced Melanoma

Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomograp...

Explainable Deep Learning for Lesion-Level Detection of Diabetic Retinopathy: A Segmentation Approach Using Fundus Images Graded as Mild-to-Moderate Nonproliferative Diabetic Retinopathy

Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...

Understanding the Relationship Between Germ Layer Origin and Cancer Therapy Response: A Systematic Review

Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malig...

Modelling Approaches for Predicting the Distribution of Skin NTDs: A Systematic Review

Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions in endemic r...

Advancing Breast Cancer-AI Diagnostics: An Explainable Deep Learning Model Using 2D Grayscale Ultrasound Imaging

Breast cancer stands as the primary reason for fatality in female patients from cancer worldwide. The diagnostic precision of ultrasound imaging depen...

Evaluating Large Language Models in Interpreting Cervical Cytology

Large language models (LLMs) have shown promise in medical imaging, but their utility in cytology remains underexplored. This study evaluates GPT-5 an...

Deep Learning-Based Classification of Melanoma and Cutaneous Lesions Using NFNet Architecture: Development and Clinical Validation

Melanoma remains the most lethal form of skin cancer, necessitating early detection for optimal patient outcomes. This study presents an advanced auto...

The impact of a SmartPhone applicatiOn for skin cancer risk assessmenT on the healthcare system (SPOT-study): A randomized controlled trial

Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general populat...

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWAS

MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but their availability is limited by cost and throughpu...

Deep learning based ischemic lesion markers on non-contrast head CT compared to CTP and DWI

Quantification of ischemic brain tissue on non-contrast CT (NCCT) in acute ischemic stroke is challenging in the acute setting. To compare the spatial...

The emergence of superficial dermatophytosis due to Trichophyton indotineae and Trichophyton mentagrophytes genotypes VII and II* in the United States: A need for comprehensive testing approaches

We report an exponential rise in dermatophyte infections belonging to the Trichophyton interdigitale/mentagrophytes species complex (TiTmSC), includin...

DeepFLAIR*: Improving Multiple Sclerosis Diagnostic Imaging Workflow Using Deep Learning

Magnetic resonance imaging (MRI) plays a central role in diagnosing multiple sclerosis (MS), yet conventional T2-FLAIR imaging provides limited specif...

Topological Feature Fusion for Dermoscopic Skin Cancer Detection

Skin cancer is a common and potentially fatal disease where early detection can save lives, especially for melanoma. Current deep learning systems cla...