ArticleWorld journal of gastroenterology2025
Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundComputer-aided diagnosis (CAD) may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.
aimTo build a system using CAD to detect and classify polyps based on the Yamada classification.
methodsA 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.
resultsComputer-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.
conclusionWe 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.
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