July 2025 in “Harvard Dataverse” A deep learning model accurately detects early hair loss signs using scalp images.
July 2025 in “The Ewha Medical Journal” This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.
This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.
67 citations
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December 2015 in “Journal of the National Comprehensive Cancer Network” This review summarizes NCCN guidelines for prostate cancer early detection, focusing on strategies to maximize detection of potentially curable cases while minimizing unnecessary procedures, but it reports no new clinical findings.
50 citations
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February 2007 in “Clinical obstetrics and gynecology” This review discusses the clinical manifestations and diagnostic challenges of polycystic ovary syndrome in adolescents, emphasizing the importance of early diagnosis and intervention to manage symptoms effectively, but it reports no new clinical results.