This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.
3 citations
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August 2024 in “Applied Sciences” In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.
40 citations
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June 2023 in “Dermatology and Therapy” This study found that the emotional impact of alopecia areata is significant, but it may not correlate directly with the extent of hair loss since some individuals adapt to the condition.
4 citations
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October 2022 in “Journal of Imaging” This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.
May 2022 in “Dermatology practical & conceptual” This study developed new graded visual scales integrating macroscopic and trichoscopic images for assessing the severity of androgenetic alopecia in men and women, potentially aiding in more objective disease evaluation.
