Introduction

Computer-aided diagnosis (CAD) systems are widely implemented in the medical field for the analysis of medical images. PolyDeep is a CAD system that has demonstrated its efficacy in lesion detection.

Aims & Methods

The aim of this study is to determine whether PolyDeep increases the adenoma detection rate (ADR) in the context of expert endoscopists participating in a regional, population-based colorectal cancer screening program in Spain. PolyDeep Advance 3 (NCT05513261) is a multicentric, randomized controlled clinical trial. For the primary endpoint, we aimed to assess whether the ADR is superior in PolyDeep-assisted colonoscopy compared to conventional colonoscopy. As secondary endpoints, we aimed to determine whether PolyDeep-assisted colonoscopy improves the polyp detection rate (PDR), serrated lesion detection rate (SLDR), advanced serrated lesion detection rate (ASLDR), and advanced adenoma detection rate (AADR). We included participants who underwent colonoscopy for screening (i.e. those with a positive faecal immunochemical occult blood test – FIT+) or for surveillance after resection of advanced adenomas. We compared both groups using the chi-square and t-Student test.

Results

Between November 2023 and May 2024, we recruited 857 patients, of whom 30 were excluded. The baseline characteristics of the 827 patients included in the analysis were similar (male: 59.3%, screening colonoscopy: 66.7%, mean age: 61.7 ± 6.5 years, withdrawal time: 10:49 ± 04:32 minutes, colorectal cancer incidence: 2.8%). However, there were significant differences between both groups in the Boston Bowel Preparation Scale (conventional colonoscopy: 7.7 ± 1.3 vs. PolyDeep-assisted colonoscopy: 7.4 ± 1.3, p = 0.01). Regarding ADR, we did not find statistically significant differences between conventional and PolyDeep-assisted colonoscopy (57.7% vs. 60.8%, p = 0.4). Similarly, for the secondary endpoints, there were no significant differences in PDR (68.7% vs. 69.6%, p = 0.8), SLDR (32.0% vs. 29.4%, p = 0.5), ASLDR (5.3% vs. 5.1%, p = 1.0), or AADR (24.5% vs. 23.6%, p = 0.8). In Table 1, we described lesion detection rates between both groups by indication (i.e., FIT+ or surveillance).

Conclusion

In the context of expert endoscopists PolyDeep did not increase the ADR.

Introduction

Any computer-assisted characterization (CADx) system relies on Artificial Intelligence algorithms to provide a probability score to the endoscopist, characterizing the polyp as an adenoma or a hyperplastic polyp.^1-3 However, the clinical utility of these AI-generated outputs is dependent on the endoscopist’s ability to interpret and integrate them into their final diagnostic decision-making process. This requires not only recognizing and accepting a true positive AI diagnosis, but also accurately identifying and rejecting false positives. This requires a higher level of endoscopist expertise to discern whether an AI-generated classification is accurate and should be incorporated into the final diagnosis or requires correction. The primary challenge in CADx implementation lies in this inherent complexity of human-AI interactions, which can lead to misinterpretation, resulting in the endoscopist either rejecting correct CADx outputs or accepting incorrect classifications, compromising diagnostic accuracy.

Aims & Methods

We conducted a secondary analysis from a prospective study. Endoscopists were first presented with the CADx classification for each polyp, which they could accept or reject and provide an alternative. A total of 1143 patients aged at least 45 underwent colonoscopy between June 2022 and January 2025 at the CHUM Hospital, yielding 753 polyps from 367 patients after exclusion criteria. The primary aim was to assess the agreement/rejection patterns of the CADx diagnosis by the endoscopists, stratified by categorization. Secondary outcomes included the rate at which clinicians correctly rejected inaccurate CADx outputs, as well as trends in post-rejection reclassification.

Results

A total of 89.1% (408/458, 95% CI: (85.8,91.7)) of accepted CADx neoplastic diagnoses were correct, and 68.7% (68/99, 95% CI: (58.5,77.4)) of accepted CADx hyperplastic diagnoses were accurate. However, endoscopists accepted incorrect diagnoses significantly more often when CADx predicted hyperplastic than neoplastic (%, 95% CI: 31.3% (22.6,41.5) vs 10.9% (8.3,14.2)). Regarding the post-rejection patterns, when endoscopists rejected adenoma classifications (N=47), they falsely classified 25 cases (53.2%, 95% CI: (38.2,67.6)) as a sessile serrated lesion/polyp (SSL/P). Similarly, although 43.6% of the rejected hyperplastic CADx predictions were correctly re-assigned an adenoma (31.5%, 95% CI: (24.3,39.7)) or SSL/P diagnosis (12.1%, 95% CI: (7.5,18.7)), there were 41.6% (95% CI: (33.7,50.0)) that were incorrectly re-classified as SSL/Ps.

Conclusion

Endoscopists generally accept accurate adenoma predictions provided by CADx systems but frequently misclassify hyperplastic polyps as SSLs, demonstrating a diagnostic bias toward neoplasia. Although beneficial from a safety perspective, this bias may result in unnecessary early follow-up colonoscopies, increasing associated costs.^4,5 Developing and integrating CADx systems with robust SSL recognition capabilities seems essential to overcome these diagnostic challenges and enhance clinical decision-making when using CADx assisted optical polyp diagnosis. Such advanced CADx systems are likely crucial for successful CADx integration into routine clinical practice.

References

1. Djinbachian R, Taghiakbari M, Barkun A, et al. Optimized computer-assisted technique for increasing adenoma detection during colonoscopy: a randomized controlled trial. Surg Endosc 2025;39:1120-1127.
2. Byrne MF, Von Renteln D, Barkun AN. Artificial Intelligence-Aided Colonoscopy for Characterizing and Detecting Colorectal Polyps: Required, Nice to Have, or Overhyped? Gastroenterology 2023;164:332-333.
3. Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021;27:8103-8122.à
4. Force AAT, Parasa S, Berzin T, et al. Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointest Endosc 2025;101:2-9 e1.
5. Halvorsen N, Barua I, Kudo SE, et al. Leaving colorectal polyps in situ with endocytoscopy assisted by computer-aided diagnosis: a cost-effectiveness study. Endoscopy 2025.

Disclosure

Daniel von Renteln has received research funding from ERBE Elektromedizin GmbH, Ventage, Pendopharm, Fujifilm and Pentax, and has received consultant or speaker fees from Boston Scientific Inc., ERBE Elektromedizin GmbH, and Pendopharm. Roupen Djinbachian has received speaker fees from Fujifilm. The remaining authors declare that they have no conflict of interest.