Dermatology

Synthesizing Contrast-Enhanced T1 MR Image Using Multiparametric Sequences and Attention to Brain Tumor

The administration of gadolinium-based contrast agents (GBCAs) for acquiring contrast-enhanced T1-weighted magnetic resonance imaging (T1C MRI) is associated with potential safety risks, including tissue deposition and nephrogenic systemic fibrosis. This work aims to develop a deep learning framework for synthesizing high-fidelity T1C MRI directly from multi-parametric, non-contrast sequences, the...

Classifying and visualizing medication use in the Adolescent Brain Cognitive Development (ABCD) Study

Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to analyze due to the complexity of medication labeling and classification. We present a reproducible framework that standardizes medication categorization in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), improving analytic consistency ...

Integrating Protein-protein Interaction Networks and Machine Learning to Identify Biomarkers of Cancer Onset

Recent large-scale plasma proteomic studies have identified a set of biomarkers for the diagnosis of early cancer onset, but the predictive performanc...

Non-invasive epidermal proteomics and machine learning permits molecular subclassification of psoriasis and eczematous dermatitis

Current approaches to selecting molecularly targeted therapies (biologics and oral small molecules) for immune-mediated skin diseases largely overlook...

A Global Atlas of Digital Dermatology to Map Innovation and Disparities

The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and co...

Demographics, Overlap, and Latency of Severe Cutaneous Adverse Reactions in an FDA Database

Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS-TEN), drug reaction with eosinophilia a...

Mapping the Neuroanatomy of Dystonia Using Causal Brain Lesions

Dystonia is one of the most prevalent movement disorders, but its neural substrates have remained enigmatic despite decades of research. Brain lesions...

HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these c...

3D Bioprinting Skin Equivalents: A Methodological Perspective on Human Keratinocyte and Fibroblast Models for Wound Repair and Regeneration.

Three-dimensional (3D) bioprinting is a promising approach to developing reliable tissue substitutes for translational research. The great interest in...

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using F-FDG PET/CT parameters and machine learning: a dual-center study.

BACKGROUND: Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop indiv...

Exploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction.

OBJECTIVE: This study aims to examine association between vitamin D with melanoma and develop an explainable machine learning model.

Identification and validation of shared biomarkers and drug repurposing in psoriasis and Crohn's disease: integrating bioinformatics, machine learning, and experimental approaches.

BACKGROUND: Psoriasis and Crohn's disease (CD) are chronic inflammatory diseases that involve complex immune-mediated mechanisms. Despite clinical ove...

Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues.

BACKGROUND: Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the skin and mucous membranes. The exact aetiology an...

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition.

This study aimed to compare image quality and solid focal liver lesion (FLL) assessments between free-breathing, diffusion-weighted imaging using deep...

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.

Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2...

Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.

This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-...

Subject-Based Transfer Learning in Longitudinal Multiple Sclerosis Lesion Segmentation.

BACKGROUND AND PURPOSE: Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (M...

FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis

With the rapid advancement of deep learning technologies, artificial intelligence has become increasingly prevalent in the research and application ...

{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segment...

Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0

We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findin...