Objectives: An accurate polyp size estimation during colonoscopy is crucial to determine the surveillance interval and predict the risk of malignant progression. However, there is a high degree of subjectivity in estimating polyp size among endoscopists in clinical practice. We aimed to assess the efficacy of a novel method that uses artificial intelligence (AI) to measure the size of colon polyps and compare it with current approaches.
Methods: Using the W-Net model for vessel segmentation and based on retinal image datasets (DRIVE, STARE, CHASE-DB, and HRF) and colonoscopy images, we developed the bifurcation-to-bifurcation (BtoB) distance measuring method and applied it to endoscopic images. Measurements were compared with those obtained by eight endoscopists (four expert and four trainees). Diagnostic ability and reliability were evaluated using Lin's concordance correlation coefficients (CCCs) and Bland-Altman analyses.
Results: For both experts and trainees, visually estimated sizes of the same polyp were significantly inconsistent depending on the camera view used (P < 0.001). Bland-Altman analyses showed that there was a trend toward underestimation of the sizes of the polyps in both groups, especially for polyps larger than 10 mm. The new technique was highly accurate and reliable in measuring the size of colon polyp (CCC, 0.961; confidence interval 0.926-0.979), clearly outperforming the visual estimation and open biopsy forceps methods.
Conclusion: The new AI measurement method improved the accuracy and reliability of polyp size measurements in colonoscopy images. Incorporating AI might be particularly important to improve the efficiency of trainees at estimating polyp size during colonoscopy.
Keywords: colonoscopy; deep learning; model; polyp size; vessel.
© 2022 Japan Gastroenterological Endoscopy Society.