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.
February 2026 in “Advanced Science” This study found that the combination of TTNPB and CHIR99021 enhanced the derivation of highly advanced neural stem cells from human pluripotent stem cells, with improved chromatin accessibility and neuroectodermal gene expression, and these cells successfully engrafted in rat hippocampi to ameliorate depression-like symptoms.
7 citations
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January 2012 This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.
2 citations
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September 2025 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft” This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.
1 citations
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May 2025 in “Journal of Digital Information Management” This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.