Dermatology

Response to "Reply to 'A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases.' Has the complicated disease been simplified too much?" Artificial intelligence system is helpful for diagnosis of cutaneous lupus erythematosus.

Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels.

AIM: To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength levels.

Reply to "A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance - simplifying the diagnosis of complicated diseases": Machine learning evaluations need to consider the true clinical context.

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length an...

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through ...

Predicting the clinical management of skin lesions using deep learning.

Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine l...

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unc...

Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images.

BACKGROUND AND OBJECTIVE: The purpose of the present study was to investigate low-shot deep learning models applied to conjunctival melanoma detection...

Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.

Malignant melanoma (MM) is one of the most dangerous skin cancers. The aim of this study was to present a potential new method for the differential di...

Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell ...

Deep learning approach to skin layers segmentation in inflammatory dermatoses.

Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequenc...

Automated severity scoring of atopic dermatitis patients by a deep neural network.

Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automat...

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose T...

Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network.

Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based ph...

Personalized prediction of early childhood asthma persistence: A machine learning approach.

Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to id...

A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19.

During the COVID-19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate...

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer.

OBJECTIVE: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiolog...

Pushing the boundaries of computer-aided diagnosis of melanoma.

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains ...

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies.

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist's wor...