Dermatology

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to de...

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in...

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to...

Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been consi...

Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts.

BACKGROUND AND OBJECTIVES: One of the challenges in developing effective hair loss therapies is the ...

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

BACKGROUND: Prompt identification of patients suspected to have COVID-19 is crucial for disease cont...

Review of medical image recognition technologies to detect melanomas using neural networks.

BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class pro...

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagn...

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and...

Value of MR-based radiomics in differentiating uveal melanoma from other intraocular masses in adults.

PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) r...

The Vascular Basis of the Pronator Quadratus Muscle Flap and Its Use in Clinical Cases.

 Pronator quadratus (PQ) is a deeply situated muscle in the forearm which may occasionally be utili...

COVID-19: An opportunity to build dermatology's digital future.

Impact of tea leaves types on antioxidant properties and bioaccessibility of kombucha.

Five different tea varieties (white, green, oolong, black and pu-erh) were infused, drained and used...

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosom...

Progressive Multiple Sclerosis Transcriptome Deconvolution Indicates Increased M2 Macrophages in Inactive Lesions.

Accumulating evidence suggests M2 macrophages contribute to tissue reparation and limit inflammation...

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs.

The early screening and diagnosis of tuberculosis plays an important role in the control and treatme...

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning.

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify ...

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard.

BACKGROUND: MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (...

Automatic skin lesion classification based on mid-level feature learning.

Dermoscopic images are widely used for melanoma detection. Many existing works based on traditional ...

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (...