Wei Chen1, Yuanhao Chai2, Gang Chai1,3, Yong Hu4, Mingang Chen5, Haisong Xu3, Yan Zhang3. 1. Department of Plastic and Reconstructive Surgery, Maternal and Child Health Care Hospital of Hainan Province, Haikou, China. 2. Sonoma Academy, Santa Rosa, CA. 3. Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine. 4. Shanghai Institute of Technical, Physics of the Chinese Academy of Sciences. 5. Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, China.
Abstract
BACKGROUND: Nevus is very common; however, melanoma is slightly related to the deterioration of nevus because of its vulnerability to solarization, friction, aging, heredity, and other factors. Early diagnosis is essential for melanoma treatment, since patients have a high survival rate with early detection and treatment. Computer-aided diagnosis has been applied in the differential diagnosis of melanoma and benign nevi and achieved high accuracy, but it does not suit the screening of nevi because most studies are based on dermoscopy with a narrow field of vision and performed by professional doctors. Therefore, this study aimed to present the accuracy and effectiveness of our algorithm. METHODS: Based on whole-face images of patients, the authors used logistic regression and the Newton method to detect the nevus region. Then, Python and OpenCV were employed to detect the lesion edge and compute the area of the regions. A multicenter clinical trial with a sample size of 600 was then conducted to evaluate the effectiveness of the algorithm. RESULTS: The algorithm detected 2672 nevi from 600 patients, in which there were 195 patients of missed diagnosis and 310 patients of misdiagnosis. The Kappa value between 2 groups was 0.860 (>0.8). Paired t-test showed no significant difference between 2 groups' area results (P = 0.265, P > 0.05). CONCLUSION: Within the limitations of this study, the authors demonstrated a high agreement between algorithm's detection and doctor's diagnosis. Our new algorithm has great effectiveness in nevus detection, edge segmentation, and area measurement.
BACKGROUND: Nevus is very common; however, melanoma is slightly related to the deterioration of nevus because of its vulnerability to solarization, friction, aging, heredity, and other factors. Early diagnosis is essential for melanoma treatment, since patients have a high survival rate with early detection and treatment. Computer-aided diagnosis has been applied in the differential diagnosis of melanoma and benign nevi and achieved high accuracy, but it does not suit the screening of nevi because most studies are based on dermoscopy with a narrow field of vision and performed by professional doctors. Therefore, this study aimed to present the accuracy and effectiveness of our algorithm. METHODS: Based on whole-face images of patients, the authors used logistic regression and the Newton method to detect the nevus region. Then, Python and OpenCV were employed to detect the lesion edge and compute the area of the regions. A multicenter clinical trial with a sample size of 600 was then conducted to evaluate the effectiveness of the algorithm. RESULTS: The algorithm detected 2672 nevi from 600 patients, in which there were 195 patients of missed diagnosis and 310 patients of misdiagnosis. The Kappa value between 2 groups was 0.860 (>0.8). Paired t-test showed no significant difference between 2 groups' area results (P = 0.265, P > 0.05). CONCLUSION: Within the limitations of this study, the authors demonstrated a high agreement between algorithm's detection and doctor's diagnosis. Our new algorithm has great effectiveness in nevus detection, edge segmentation, and area measurement.