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• W4386886721 abstract "The efficacy of therapeutic modalities for hair disease can be evaluated globally by photo assessment and more precisely by phototrichogram (PTG). However, the latter procedure is laborious, time consuming, subject to inter-observer variation, and requires hair clipping.To establish an automated and patient/investigator friendly methodology enabling quantitative hair amount evaluation for daily clinical practice.A novel automated numerical algorithm (aNA) adopting digital image binarization (i.e., black and white color conversion) was invented to evaluate hair coverage and measure PTG parameters in scalp images. Step-by-step improvement of aNA was attempted through comparative analyses of the data obtained respectively by the novel approach and conventional PTG/global photography assessment (GPA).For measuring scalp hair coverage, the initial version of aNA generally agreed with the cumulative hair diameter as assessed using PTG, showing a coefficient of 0.60. However, these outcomes were influenced by the angle of hair near the parting line. By integrating an angle compensation formula, the standard deviation of aNA data decreased from 5.7% to 1.2%. Consequently, the coefficient of determination for hair coverage calculated using the modified aNA and cumulative hair diameter assessed by PTG increased to 0.90. Furthermore, the change in hair coverage as determined by the modified aNA protocol correlated well with changes in the GPA score of images obtained using clinical trials.The novel aNA method provides a valuable tool for enabling simple and accurate evaluation of hair growth and volume for clinical trials and for treatment of hair disease." @default.
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• W4386886721 date "2023-09-01" @default.
• W4386886721 modified "2023-10-02" @default.
• W4386886721 title "Invention of automated numerical algorithm adopting binarization for the evaluation of scalp hair coverage: an image analysis providing a substitute for phototrichogram and global photography assessment for hair diseases" @default.
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• W4386886721 doi "https://doi.org/10.1016/j.jdermsci.2023.09.003" @default.
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