Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance

Abstract

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.

Keywords: Yamada classification; Endoscopy; Deep learning; Artificial intelligence; Computer-aided diagnosis

Core Tip: This study developed a novel deep learning (YOLOv7-based) computer-aided detection and classification system that significantly outperformed endoscopists in both detecting colorectal polyps (96.7% precision, 95.8% recall) and classifying them morphologically via the Yamada classification (80.2% F1-score). Achieving high image-based accuracy (detection: 99.2%; classification: 97.2%), this computer-aided detection and classification system offers a powerful tool to enhance polyp identification and characterization, particularly benefiting community hospital settings.