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이석준 ( Suk Jun Lee ),전종수 ( Jong Soo Jeon ),류재연 ( Jae Yeon Ryu ),송현정 ( Hyun Jeong Song ),조윤재 ( Yoon Jae Jo ),방철환 ( Chul Hwhan Bang ),박영민 ( Young Min Park ),이지현 ( Ji Hyun Lee ) 대한피부과학회 2018 대한피부과학회지 Vol.56 No.7
Background: Acne is a chronic inflammatory disease of the pilosebaceous unit, mainly on the face. It can have various clinical manifestations and should be appropriately treated based on the severity. In Korea, the 'Korea Acne Severity Rating System (KAGS)' is a standardized index to determine the severity of acne according to specific Korean characteristics. However, the actual use of the KAGS in clinical settings has been limited. Objective: We sought to analyze whether we could effectively measure acne severity using a deep learning algorithm, which is an image learning method. Methods: Acne severity was classified into three levels of mild, moderate, and severe based on the KAGS, and learning and verification were performed using the CNN (Convolutional Neural Network), a deep learning technique. Results: GoogLeNet's Inception-v3 algorithm showed the highest accuracy at 86.7%. Conclusion: This study confirmed that the use of a deep learning algorithm may facilitate the scoring of acne severity. (Korean J Dermatol 2018;56(7):421∼425)