Neural Biomarkers for Identifying Atopic Dermatitis and Assessing Acupuncture Treatment Response Using Resting-State fMRI.

In-Seon LeeDa-Eun YoonSeoyoung LeeJae-Hwan Kang Younbyoung Chae Hi-Joon ParkJunsuk Kim

Published in: Journal of asthma and allergy (2024)

We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

Keyphrases

resting state
functional connectivity
deep learning
end stage renal disease
atopic dermatitis
ejection fraction
newly diagnosed
chronic kidney disease
prognostic factors
patient reported outcomes
machine learning
cerebral ischemia
replacement therapy