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CC BY 4.0 · Endosc Int Open 2025; 13: a25561836
DOI: 10.1055/a-2556-1836
Original article

Use of artificial intelligence to measure colorectal polyp size without a reference object

Chin-Yuan Yii  ‡
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
2   Department of Biomedical Sciences and Engineering, National Central University, Zhongli, Taiwan (Ringgold ID: RIN34911)
,
Ding-Ek Toh‡
3   Department of Gastroenterology, Flinders Medical Centre, Bedford Park, Australia (Ringgold ID: RIN14351)
4   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan (Ringgold ID: RIN63474)
,
Tzu-An Chen
5   Division of Colorectal Surgery, Department of Surgery, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Wei-Lun Hsu
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
6   Institute of Computer Science and Information Engineering, National Central University, Zhongli, Taiwan (Ringgold ID: RIN34911)
,
Huang-Jen Lai
5   Division of Colorectal Surgery, Department of Surgery, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Yin-Chen Wang
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Chang-Ru Liu
7   Nursing Department, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Yow-Chii Kuo
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Shih-Hao Young
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Fu-Ming Chang
1   Division of Gastroenterology and Hepatology, Department of Internal Medicine, Landseed International Medical Group, Taoyuan, Taiwan (Ringgold ID: RIN215168)
,
Chen Lin
2   Department of Biomedical Sciences and Engineering, National Central University, Zhongli, Taiwan (Ringgold ID: RIN34911)
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Abstract

Background and study aims

Polyp size is crucial for determining colonoscopy surveillance intervals. We present an artificial intelligence (AI) model for colorectal polyp size measurement without a reference object.

Methods

The regression model for polyp size estimation was developed using outputs from two SegFormer models, segmentation and depth estimation. Initially built on colonoscopic images of polyp phantoms, the model underwent transfer learning with 1,304 real-world images. Testing was conducted on 178 images from 52 polyps, independent of the training set, using a snare as the ground truth for size comparison with the AI-based model. Polyps were classified into three size groups: ≤ 5 mm, 5–10 mm, and ≥ 10 mm. Error rates were calculated to evaluate discrepancies in actual size values between the AI model and the snare method. Precision indicated the positive predictive value per size group and recall and Bland-Altman were also conducted.

Results

The Bland-Altman analysis showed a mean bias of –0.03 mm between methods, with limits of agreement from –1.654 mm to 1.596 mm. AI model error rates for actual size discrepancies were 10.74%, 12.36%, and 9.89% for the ≤ 5 mm, 5–10 mm, and ≥ 10 mm groups, respectively, averaging 11.47%. Precision values were 0.870, 0.911, and 0.857, with overall recall of 0.846.

Conclusions

Our study shows that colorectal polyp size measurement by AI model is practical and clinically useful, exhibiting low error rates and high precision. AI shows promise as an accurate tool for measurement without the need for a reference object during screening colonoscopy.

Keywords

Endoscopy Lower GI Tract - Polyps / adenomas / ... - CRC screening - Colorectal cancer

Supplementary Material



Publikationsverlauf

Eingereicht: 19. September 2024

Angenommen nach Revision: 27. Februar 2025

Artikel online veröffentlicht:
12. Mai 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Bibliographical Record
Chin-Yuan Yii, Ding-Ek Toh, Tzu-An Chen, Wei-Lun Hsu, Huang-Jen Lai, Yin-Chen Wang, Chang-Ru Liu, Yow-Chii Kuo, Shih-Hao Young, Fu-Ming Chang, Chen Lin. Use of artificial intelligence to measure colorectal polyp size without a reference object. Endosc Int Open 2025; 13: a25561836.
DOI: 10.1055/a-2556-1836
 

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