Clinical implications of computer-aided real-time size estimation of colorectal polyps during colonoscopy: a prospective study

Abstract

Background

Accurate polyp size estimation during colonoscopy is crucial for clinical decision making, follow-up, and implementation of cost-saving strategies. Objective sizing methods are lacking, and interobserver variability is high. This prospective, multicenter, study evaluated the accuracy of a novel artificial intelligence (AI)-based algorithm for polyp size estimation.

Methods

Patient aged ≥18 years undergoing colonoscopy for colorectal cancer (CRC) screening or surveillance were enrolled across three centers. Polyp size was initially assessed by operators using forceps/snare comparison (ground truth). Procedures were recorded, and AI-based polyp size estimates were obtained offline. The primary outcome was AI accuracy in size class determination (diminutive ≤5 mm, small 6–9 mm, large ≥10 mm). Secondary outcomes included size estimation in mm and impact on clinical management strategies.

Results

Among 465 polyps (307 diminutive, 107 small, 51 large) from 217 patients (mean age 61.9 [SD 10.4] years, 51.6% female), AI accuracy for size class determination was 85.8% (95%CI 82.5–88.8). Accuracy for diminutive, small, and large polyps was 93.3%, 74.6%, and 55.1%, respectively. The AI tool assigned 90.8% of patients to correct surveillance intervals and achieved mean absolute error of 1.13 mm and root mean square error of 1.40 mm for polyps ≤10 mm.

Conclusions

The AI model performed similarly to expert endoscopists in clinically relevant size-related outcomes, potentially improving the accuracy and efficiency of CRC screening.

Publication History

Received: 03 May 2025

Accepted after revision: 29 August 2025

Accepted Manuscript online:
03 September 2025

Article published online:
02 October 2025

• References

• 1 Rex DK, Kahi C, O’Brien M. et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2011; 73: 419-422
• 2 Houwen BBSL, Hassan C, Coupé VMH. et al. Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 88-99
• 3 Ferlitsch M, Hassan C, Bisschops R. et al. Colorectal polypectomy and endoscopic mucosal resection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – update 2024. Endoscopy 2024; 56: 516-545
• 4 Hassan C, Repici A, Rex D. Addressing bias in polyp size measurement. Endoscopy 2016; 48: 881-883
• 5 Buijs MM, Steele RJC, Buch N. et al. Reproducibility and accuracy of visual estimation of polyp size in large colorectal polyps. Acta Oncol 2019; 58: S37-41
• 6 Taghiakbari M, Djinbachian R, Labelle J. et al. Endoscopic size measurement of colorectal polyps: a systematic review of techniques. Endoscopy 2025; 57: 460-477
• 7 Djinbachian R, Taghiakbari M, Mubaid F. et al. Establishing ground truth of polyp size, morphology, and volume using three-dimensional scanning. Endoscopy 2023; 55: E1260-1261
• 8 Djinbachian R, Taghiakbari M, Alj A. et al. Virtual scale endoscope versus snares for accuracy of size measurement of smaller colorectal polyps: a randomized controlled trial. Endoscopy 2025; 57: 443-450
• 9 Sounderajah V, Ashrafian H, Golub RM. et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021; 11: e047709
• 10 Antonelli G, Libanio D, De Groof AJ. et al. QUAIDE – quality assessment of AI preclinical studies in diagnostic endoscopy. Gut 2024; 74: 153-161
• 11 Lai EJ, Calderwood AH, Doros G. et al. The Boston bowel preparation scale: a valid and reliable instrument for colonoscopy-oriented research. Gastrointest Endosc 2009; 69: 620-625
• 12 The Paris endoscopic classification of superficial neoplastic lesions. esophagus, stomach, and colon: November 30 to December 1, 2002. Gastrointest Endosc 2003; 58: S3-43
• 13 Schlemper RJ, Riddell RH, Kato Y. et al. The Vienna classification of gastrointestinal epithelial neoplasia. Gut 2000; 47: 251-255
• 14 Hassan C, Antonelli G, Dumonceau J-M. et al. Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – update 2020. Endoscopy 2020; 52: 687-700
• 15 Wang J, Li Y, Chen B. et al. A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation. Endoscopy 2024; 56: 260-270
• 16 Plumb AA, Nickerson C, Wooldrage K. et al. Terminal digit preference biases polyp size measurements at endoscopy, computed tomographic colonography, and histopathology. Endoscopy 2016; 48: 899-908
• 17 Messmann H, Bisschops R, Antonelli G. et al. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 1211-1231