1 citations
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March 2024 in “Skin research and technology” In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.
5 citations
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July 2023 in “Journal of Autonomous Intelligence” This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.
January 2024 in “Wiadomości Lekarskie” This source reports that clinical trials using advanced Deep Brain Stimulation systems, augmented with AI to integrate kinematic data, eye tracking, and cognitive assessments, show promise in improving diagnostic accuracy and monitoring symptoms for patients with Parkinson's disease.
May 2026 in “Case Reports in Dermatology” This case report detailed a 31-year-old kidney transplant recipient who experienced significant hair loss linked to tacrolimus-based immunosuppression, which improved after switching to cyclosporine A, emphasizing the importance of awareness and management of hair-related side effects in transplant patients.
1 citations
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August 2023 in “arXiv (Cornell University)” This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.
