November 2023 in “Journal of Dermatological Science” A new computer tool quickly measures hair thickness differences in people with common types of hair loss.
November 2024 in “Image Analysis & Stereology” The method improves hair image segmentation accuracy while reducing annotation costs.
January 2024 in “Lecture notes in networks and systems” "TRICHOASSIST" is a system that analyzes hair and scalp images to help diagnose scalp diseases.
April 2023 in “Journal of Investigative Dermatology” A new image-based method improves accuracy in measuring hair loss in mice.
6 citations
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August 2016 in “Journal of Visualized Experiments” The CUBIC protocol allows detailed 3D visualization of proteins in mouse skin biopsies.
June 2025 in “British Journal of Dermatology” ALUDWIG can help standardize female hair loss assessment from a single image.
21 citations
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January 2010 in “International Journal of Trichology” TrichoScan often makes mistakes and needs improvement for correct hair growth analysis.
10 citations
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January 2020 in “Royal Society Open Science” A new automated method accurately measures hair damage using microscopic images.
October 2021 in “bioRxiv (Cold Spring Harbor Laboratory)” The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.
2 citations
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August 2006 in “Journal of Dermatological Science” Automated image analysis helps diagnose and monitor alopecia areata by efficiently measuring hair follicles.
An automated system can accurately classify hair disorders using image analysis.
40 citations
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April 2006 in “Journal of the European Academy of Dermatology and Venereology” The Trichoscan system was found to be inaccurate for measuring hair growth, needing better software to be useful.
1 citations
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March 2015 in “Journal of Visualized Experiments” Researchers developed a new, precise method to measure hair loss in mice using image analysis.
November 2022 in “The journal of investigative dermatology/Journal of investigative dermatology” New imaging technology can show up to 40 different markers in hair loss tissue, helping to understand hair disease better.
March 2026 in “Mendeley Data” March 2026 in “Mendeley Data” January 2017 in “British journal of dermatology/British journal of dermatology, Supplement” September 2017 in “Journal of Investigative Dermatology” QMSI effectively maps and quantifies drug distribution in skin tissues.
9 citations
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January 2020 in “IEEE Access” The KEBOT system is a highly accurate AI tool for analyzing hair transplants.
March 2015 in “Journal of Visualized Experiments” A new method measures mouse hair loss using shades of gray.
5 citations
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February 2015 in “Dermatologica Sinica” Computer-aided imaging system helps measure balding area in female pattern hair loss.
The tool accurately measures hair count and size on scalps with 79.45% and 68.19% accuracy, respectively.
September 2025 in “The Open Dermatology Journal” The AI showed high accuracy in diagnosing skin conditions but needs improvement for immunological and infectious disorders.
1 citations
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January 2026 in “GigaScience” Cell Journey is a tool for better 3D visualization of cell changes over time.
December 2022 in “Research Square (Research Square)” The QuantAnts machines can find cancer markers and create CRISPR targets for them.
April 2017 in “The journal of investigative dermatology/Journal of investigative dermatology” QMSI is a valuable method for studying drug penetration in skin tissues.
1 citations
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December 2024 in “Dermatology and Therapy” The STRIAA tool helps doctors quickly and effectively assess the severity of Alopecia Areata.
September 2020 in “Zenodo (CERN European Organization for Nuclear Research)” Reducing Zyxin may help treat hair loss.
January 2018 in “Communications in computer and information science” Researchers developed a computer system to automatically diagnose hair loss by analyzing scalp images.