Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor

Abstract

Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy videos to enhance AI model performance and reduce manual labeling labor. Methods: An integrated AI model was trained and validated on 86,258 training images and 17,616 validation images. Model 1 utilized only publicly available datasets, while Model 2 additionally incorporated images obtained from real colonoscopy videos of patients through a semi-automatic annotation process, significantly reducing the labeling burden on expert endoscopists. Results: The integrated AI model (Model 2) significantly outperformed the public-dataset-only model (Model 1). At epoch 35, Model 2 achieved a sensitivity of 90.6%, a specificity of 96.0%, an overall accuracy of 94.5%, and an F1 score of 89.9%. All polyps in the test videos were successfully detected, demonstrating considerable enhancement in detection performance compared to the public-dataset-only model. Conclusions: Integrating real-world colonoscopy video data using semi-automatic annotation markedly improved diagnostic accuracy while potentially reducing the need for extensive manual annotation typically performed by expert endoscopists. However, the findings need validation through multicenter external datasets to ensure generalizability.

Keywords: artificial intelligence; colon cancer; colon polyp; colonoscopy.

Figure 1

Process of developing the initial model with public datasets and constructing additional datasets.

Figure 2

Examples of resulting images from inference in Model 1. The images include detected polyps (a) and normal tissue (b). Blue squares indicate data selected for further training.

Figure 3

Training loss and validation accuracy per epoch for Model 1 and Model 2.

Figure 4

Example of polyps (blue circle) that were not detected by either Model 1 (a) or Model 2 (b) among 120 test polyps.

Figure 5

The number of TPs for polyps in each epoch. The X-axis represents the number of TPs, while the Y-axis indicates the number of polyps.