Dermatology

Skin Phototype Classification with Machine Learning Based on Broadband Optical Measurements.

The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may contribute to dermatological care disparities in patients with darker skin phototypes, including the misdiagnosis of wound healing progression and esca...

Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty.

PURPOSE: The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE) in terms of image quality, lesion conspicuity, and lesion detection.

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

SIGNIFICANCE: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair...

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyr...

Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.

This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of whi...

Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.

OBJECTIVE: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radi...

Segmentation of breast lesion using fuzzy thresholding and deep learning.

Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (...

Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures.

Patient education materials (PEMs) are crucial for improving patient adherence and outcomes; however, they may not be accessible due to high reading l...

Using bioinformatics and artificial intelligence to map the cyclin-dependent kinase 4/6 inhibitor biomarker landscape in breast cancer.

A cyclin-dependent kinase 4/6 (CDK4/6) inhibitor combined with endocrine therapy is the standard-of-care for patients with hormone receptor-positive/h...

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images.

Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated...

The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.

BACKGROUND: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five...

Transforming Skin Cancer Diagnosis: A Deep Learning Approach with the Ham10000 Dataset.

Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has the deadliest potential to spread to other parts of the body among al...

Weakly Supervised Classification of Mohs Surgical Sections Using Artificial Intelligence.

Basal cell carcinoma (BCC) is the most frequently diagnosed form of skin cancer, and its incidence continues to rise, particularly among older individ...

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) u...

AI in Aesthetic/Cosmetic Dermatology: Current and Future.

BACKGROUND: Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. How...

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models.

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of chara...

Reliable and easy-to-use calculating tool for the Nail Psoriasis Severity Index using deep learning.

Since nail psoriasis restricts the patient's daily activities, therapeutic intervention based on reliable and reproducible evaluation is critical. The...

Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images.

Early detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identif...

Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.

Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal func...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopath...