Development of an Artificial Intelligence for Detecting False Positive Detection to Manage the Accuracy of Artificial Intelligence in Colonoscopy.

Objectives: False positive detection (FPD) is a clinically relevant challenge that must be solved to ensure the safe and effective integration of artificial intelligence (AI) into colonoscopy. This study aimed to develop a novel AI system for quantifying FPD and to compare FPD rates between two commercially available AI platforms. Methods: From October 2022 to May 2025, a total of 436,637 images were collected from 78 patient colonoscopy videos: 38 videos (340,121 images) using CAD EYE (Fujifilm, Tokyo, Japan) and 40 videos (96,516 images) using EndoBRAIN (Olympus, Tokyo, Japan). Using these images, YOLOv8 convolutional neural networks were applied to construct original AI model for FPD analysis: ARBS-YOLOv8 (with active learning). The performance of the model was validated using the CAD EYE and EndoBRAIN clinical videos. Results: For CAD EYE validation, ARBS-YOLOv8 achieved an accuracy, sensitivity, specificity, precision, and F1 score of 99.22%, 99.51%, 99.97%, 99.96%, and 99.90%, respectively. For EndoBRAIN validation, the ARBS-YOLOv8 results were 99.97%, 99.84%, 99.97%, 99.97%, and 99.90%, respectively. The false-positive and false-negative rates of FPD detection were only 0.04% and 0.03% for CAD EYE and 0.03% and 0.03% for EndoBRAIN. In 13 collected clinical videos (7 CAD EYE, 6 EndoBRAIN), ARBS-YOLOv8 identified total FPD rates of 8.08% for CAD EYE and 2.47% for EndoBRAIN, showing a significant difference (p<0.001). Conclusion: We developed the first AI system capable of objectively measuring FPD during colonoscopy under two commercially available AI platforms. The model validated performance differences between CAD EYE and EndoBRAIN, providing a basis for FPD reduction and safe AI endoscopy.