AIMC Topic: Dermatitis, Atopic
Clear Filters Showing 11 to 20 of 29 articles
Quantifying Inflammatory Response and Drug-Aided Resolution in an Atopic Dermatitis Model with Deep Learning.
The Journal of investigative dermatology
Feb 17, 2023
Noninvasive quantification of dermal diseases aids efficacy studies and paves the way for broader enrollment in clinical studies across varied demographics. Related to atopic dermatitis, accurate quantification of the onset and resolution of inflamma...
Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation.
Computers in biology and medicine
Mar 17, 2022
Atopic dermatitis (AD) is a common inflammatory skin disease involving multiple signaling pathways. One of the effective treatment strategies of AD is to develop a new drug capable of regulating the key therapeutic targets. Here we report the combina...
Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.
Scientific reports
Jan 7, 2022
Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to de...
Deep learning approach to skin layers segmentation in inflammatory dermatoses.
Ultrasonics
Mar 21, 2021
Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequency ultrasound (HFUS) makes it possible to monitor skin condition in different dermatoses. Accurate an...
Automated severity scoring of atopic dermatitis patients by a deep neural network.
Scientific reports
Mar 15, 2021
Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has n...
Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases.
The Journal of investigative dermatology
Feb 9, 2021
Automated detection of mouse scratching behaviour using convolutional recurrent neural network.
Scientific reports
Jan 12, 2021
Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited ...
The RoScan Thermal 3D Body Scanning System: Medical Applicability and Benefits for Unobtrusive Sensing and Objective Diagnosis.
Sensors (Basel, Switzerland)
Nov 20, 2020
The RoScan is a novel, high-accuracy multispectral surface scanning system producing colored 3D models that include a thermal layer. (1) Background: at present, medicine still exhibits a lack of objective diagnostic methods. As many diseases involve ...
Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.
Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology
Sep 9, 2020
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms ...
Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis.
Scientific reports
May 14, 2020
The diagnostic possibilities of multiphoton tomography (MPT) in dermatology have already been demonstrated. Nevertheless, the analysis of MPT data is still time-consuming and operator dependent. We propose a fully automatic approach based on convolut...
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