A new CNN model can detect Alopecia Areata with 98% accuracy.
10 citations
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September 2020 in “Computational and Mathematical Methods in Medicine” Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
7 citations
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October 2023 in “Journal of Intelligent & Fuzzy Systems” The new model improves Alopecia Areata classification accuracy to 93.1%.
4 citations
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May 2024 in “INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT” AI can accurately diagnose hair and scalp conditions and suggest treatments.
3 citations
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January 2019 in “Electronic Imaging” The device accurately estimates natural hair color at the roots in real time.
3 citations
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December 2018 in “Meta Gene” Certain gene variations increase male hair loss risk, influenced by hormone levels.
2 citations
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January 2024 in “Journal of Emerging Investigators” A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
2 citations
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January 2024 in “IEEE Access” AlopeciaDet accurately detects Alopecia Areata early using advanced image analysis.
1 citations
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March 2024 in “arXiv (Cornell University)” Deep learning can effectively detect hair and scalp diseases early.
1 citations
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August 2023 in “arXiv (Cornell University)” Deep learning effectively diagnoses scalp disorders, but improvements are needed.
1 citations
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December 2022 in “JAMA Dermatology” The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
March 2026 in “Applied Sciences” AI in hair and scalp analysis shows promise but lacks real-world clinical integration and validation.
November 2025 in “Kufa Journal of Engineering” AI can effectively detect hair and scalp disorders from images.
AI can improve alopecia areata diagnosis with high accuracy.
July 2025 in “The Ewha Medical Journal” The model accurately detects early-stage hair loss using images.
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
Transfer learning with three neural network architectures accurately classifies hair diseases.
GoogLeNet is the best model for identifying folliculitis.
January 2021 in “Lecture notes in networks and systems” Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
November 1966 in “British Journal of Dermatology” The meeting discussed various skin conditions, treatments, and unusual cases, highlighting the effectiveness of tetracycline in treating rosacea.
December 2021 in “Acta dermato-venereologica” A deep learning model accurately predicts male hair loss types using scalp images.
5 citations
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June 2023 in “Engineering Technology & Applied Science Research” The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
2 citations
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January 1980 in “Acupuncture & electro-therapeutics research” Hair loss might be due to nerve issues, treatable with electric stimulation or acupuncture.
7 citations
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January 2017 in “Stem Cells International” Neural organoids show promise for future CNS disease treatments.
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
15 citations
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December 2015 in “PLoS ONE” Fibroblasts can be mistaken for neural cells, so functional validation is needed.
2 citations
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June 2019 in “The Journal of Dermatology” Two cases showed skin abnormalities without bone or neural defects.
January 2005 in “Zhonghua xingwei yixue yu naokexue zazhi” Selenium and iodine deficiencies cause delayed growth and abnormal neural behavior in rats.
January 2008 in “Durham e-Theses (Durham University)” Hair follicle stem cells are similar to mesenchymal stem cells and can become neural-like cells under certain conditions.
1 citations
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June 2024 in “Skin Research and Technology” Human dermal fibroblast proteins help restore nerves during healing.