• Yen-Po Wang, Ying-Chun Jheng, Ming-Chih Hou, and Ching-Liang Lu.
• Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taiwan; Division of Gastroenterology, Taipei Veterans General Hospital, Taiwan; Institute of Brain Science, National Yang Ming Chiao Tung University School of Medicine, Taiwan; Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taiwan.
• J Formos Med Assoc. 2024 Dec 26.

BackgroundThe methodology in colon polyp labeling in establishing database for ma-chine learning is not well-described and standardized. We aimed to find out the best annotation method to generate the most accurate model in polyp detection.Methods3542 colonoscopy polyp images were obtained from endoscopy database of a tertiary medical center. Two experienced endoscopists manually annotated the polyp with (1) exact outline segmentation and (2) using a standard rectangle box close to the polyp margin, and extending 10%, 20%, 30%, 40% and 50% longer in both width and length of the standard rectangle for AI modeling setup. The images were randomly divided into training and validation sets in 4:1 ratio. U-Net convolutional network architecture was used to develop automatic segmentation machine learning model. Another unrelated verification set was established to evaluate the performance of polyp detection by different segmentation methods.ResultsExtending the bounding box to 20% of the polyp margin represented the best performance in accuracy (95.42%), sensitivity (94.84%) and F1-score (95.41%). Exact outline segmentation model showed the excellent performance in sensitivity (99.6%) and the worst precision (77.47%). The 20% model was the best among the 6 models. (confidence interval = 0.957-0.985; AUC = 0.971).ConclusionsLabelling methodology affect the predictability of AI model in polyp detection. Extending the bounding box to 20% of the polyp margin would result in the best polyp detection predictive model based on AUC data. It is mandatory to establish a standardized way in colon polyp labeling for comparison of the precision of different AI models.Copyright © 2024 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.

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