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research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study

2 citations , 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.

research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures

1 citations , 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.

research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

1 citations , 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.

research A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions

13 citations , February 2025 in “Nature Communications”

In this study, a deep neural network model called regX was developed to prioritize driver regulators for cell state transitions by incorporating gene-level regulation and interactions, showing potential therapeutic targets in type 2 diabetes and hair follicle development when applied to single-cell multi-omics data.

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

8 citations , January 2022 in “Sensors”

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

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Compressed part of research of theory of androgenic/anabolitic balance. AGA h-responders analytic. Theory of physio-metabolitic method of anti AGA treatment

community Compressed part of research of theory of androgenic/anabolitic balance. AGA h-responders analytic. Theory of physio-metabolitic method of anti AGA treatment

in Research 865 upvotes 1 year ago

The treatment for androgenetic alopecia involves using finasteride and minoxidil with intense exercise and cold exposure to boost metabolism and reduce androgenic effects, potentially leading to hair regrowth. This approach may activate biological pathways for improved hair and overall health.

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research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study

research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures

research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

research A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

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community Compressed part of research of theory of androgenic/anabolitic balance. AGA h-responders analytic. Theory of physio-metabolitic method of anti AGA treatment

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