Microbial imbalances on the scalp can help diagnose and manage hair loss early.
6 citations
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September 2025 in “Scientific Reports” Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.
1 citations
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August 2023 in “arXiv (Cornell University)” Deep learning effectively diagnoses scalp disorders, but improvements are needed.
The system can automatically identify different hair and scalp conditions using machine learning.
June 2025 in “British Journal of Dermatology” The new AI software predicts melanoma outcomes more accurately than traditional methods.
December 2024 in “International Journal of experimental research and review” Adding obesity data to machine learning models improves heart disease prediction accuracy.
5 citations
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October 2023 in “International Journal on Recent and Innovation Trends in Computing and Communication” The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
January 2021 in “Lecture notes in networks and systems” Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
December 2020 in “Journal of The American Academy of Dermatology” Artificial intelligence can accurately predict hair growth and treatment results in female pattern hair loss patients, with age of onset and duration being key factors.
A hat with sensors can measure scalp moisture well, helping with hair care.
January 2026 in “Microsystems & Nanoengineering” New technologies replicate human skin for testing without animals.
Machine learning improves DNA predictions for eye and hair color, but challenges remain for skin tone and facial features.
5 citations
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July 2023 in “Journal of Autonomous Intelligence” Artificial neural networks can accurately diagnose Alopecia Areata.
The model predicts minoxidil's effectiveness and side effects better than traditional methods.
Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.
1 citations
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September 2024 in “arXiv (Cornell University)” Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.
2 citations
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May 2025 in “Diagnostics” ATR-FTIR spectroscopy could help monitor alopecia areata treatment response non-invasively.
The model accurately predicts hair loss by analyzing various factors.
November 2023 in “Advances and Applications in Statistics” AI can effectively predict COVID-19 mortality risk using patient data.
June 2023 in “International journal on recent and innovation trends in computing and communication” Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
November 2025 in “Clinical and Translational Medicine” DNAJB9 cfRNA could help diagnose and treat female hair loss.
1 citations
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November 2023 in “Research Square (Research Square)” DiZyme accurately predicts nanozyme activities to aid in discovering new applications.
1 citations
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January 2022 in “Electronic Imaging” A new method accurately captures and renders hair color for virtual reality and hair dye use.
March 2026 in “International Journal of Science Strategic Management and Technology” WomenCare helps predict PCOD risk in women to encourage early medical consultation.
February 2022 in “arXiv (Cornell University)” A new method accurately captures and renders hair color for real and synthetic images.
November 2025 in “Psychoneuroendocrinology” Hair proteomics could be a useful, non-invasive tool for identifying stress-related disorders.
Spaceflight can harm skin health, but organisms can adapt after returning to Earth.
4 citations
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December 2024 in “Protein & Cell” MultiKano accurately identifies cell types in complex data better than existing methods.
2 citations
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March 2025 in “PNAS Nexus” Raman spectroscopy can detect radiation exposure in mouse hair with high accuracy for up to 7 days.
December 2025 in “Cosmetics” Gut bacteria differences could help diagnose and treat alopecia areata.