World journal of gastroenterology · 2025
Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.
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Qiu Lin, Ding Jian, Lai Chun-Xiao, Yang Hui, Li Feng, Li Zhi-Jian, Wu Wen, Liu Gui-Ming, Guan Quan-Sheng, Zhang Xi-Gang, Zhang Rui-Ya, Yi Li-Zhi, Zhao Zhi-Fang, Deng Lv, Lun Wei-Jian, Wang Zhen-Yu, Lu Wei-Ming, Qiao Wei-Guang, Wang Su-Ling, Chen Si-Mei, Shen Wen-Qian, Cheng Li-Min, Zhu Ben-Gui, He Shun-Hui, Dai Jie, Bai Yang
Аннотация
BACKGROUND: Computer-aided diagnosis (CAD) may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.
AIM: To build a system using CAD to detect and classify polyps based on the Yamada classification.
METHODS: A total of 24045 polyp and 72367 nonpolyp images were obtained. We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm. Frame-based and image-based evaluation metrics were employed to assess the performance.
RESULTS: Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%, a recall of 95.8%, and an F1-score of 96.2%, outperforming those of all groups of endoscopists. In regard to the Yamada classification of polyps, the CAD system displayed a precision of 82.3%, a recall of 78.5%, and an F1-score of 80.2%, outperforming all levels of endoscopists. In addition, according to the image-based method, the CAD had an accuracy of 99.2%, a specificity of 99.5%, a sensitivity of 98.5%, a positive predictive value of 99.0%, a negative predictive value of 99.2% for polyp detection and an accuracy of 97.2%, a specificity of 98.4%, a sensitivity of 79.2%, a positive predictive value of 83.0%, and a negative predictive value of 98.4% for poly Yamada classification.
CONCLUSION: We developed a novel CAD system based on a deep neural network for polyp detection, and the Yamada classification outperformed that of nonexpert endoscopists. This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.
AIM: To build a system using CAD to detect and classify polyps based on the Yamada classification.
METHODS: A total of 24045 polyp and 72367 nonpolyp images were obtained. We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm. Frame-based and image-based evaluation metrics were employed to assess the performance.
RESULTS: Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%, a recall of 95.8%, and an F1-score of 96.2%, outperforming those of all groups of endoscopists. In regard to the Yamada classification of polyps, the CAD system displayed a precision of 82.3%, a recall of 78.5%, and an F1-score of 80.2%, outperforming all levels of endoscopists. In addition, according to the image-based method, the CAD had an accuracy of 99.2%, a specificity of 99.5%, a sensitivity of 98.5%, a positive predictive value of 99.0%, a negative predictive value of 99.2% for polyp detection and an accuracy of 97.2%, a specificity of 98.4%, a sensitivity of 79.2%, a positive predictive value of 83.0%, and a negative predictive value of 98.4% for poly Yamada classification.
CONCLUSION: We developed a novel CAD system based on a deep neural network for polyp detection, and the Yamada classification outperformed that of nonexpert endoscopists. This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.