Introduction
An accurate optical diagnosis 1,2 of colorectal polyps could make colonoscopy more cost-effective and reduce the risks of polypectomy. In a few recent retrospective studies, it was shown that artificial intelligence (AI) could be used to find and classify colorectal polyps. Many of these studies were based on still endoscopic images or video recordings rather than their application in real-time colonoscopy procedures. We conducted an open-label, non-randomized, prospective, single-center study with the aim to validate the performance of a novel state-of-the-art Artificial Intelligence (AI) model for colorectal lesion detection during routine diagnostic colonoscopy and evaluate its feasibility in daily endoscopy.
Aims & Methods
The study is a prospective, non-randomized, single-center clinical evaluation of the real-time performance of the deep learning AI model for colorectal lesion detection in colonoscopy procedures. Under white light and NBI, the primary endoscopist did the colonoscopies with high-definition endoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). All of the polyps were marked for size (measured with biopsy forceps), location, and shape based on the Paris classification, and then they were either removed or a biopsy sample was taken for histologic analysis. (Fig. 1). The main goal of this study was to figure out the "adenoma miss rate," which is the number of patients whose adenomas were missed by the endoscopists but found by the AI and then confirmed by the endoscopists on re-examination (and, as a gold standard, by histological examination). The mean and the total number of missed lesions were analyzed.
Results
Totally, 81 lesions were detected and removed from the 42 subjects, out of the n=200 colonoscopies done in March 2023. Of the 81 lesions removed from subjects, 78 (96.3%) lesions were examined, including 27 (33.3%) neoplastic lesions and 47 (60.5%) non-neoplastic lesions, and the other 3 (3.7%) lesions were not examined after snaring. As for finding lesions, the AI module found 81 of them (100%), while endoscopists found 61 of them (75.3%). Of the 20 lesions missed by the endoscopists, 5 (38.5%) were missed in the transverse colon, which was the section with the highest missing rate. Based on final histology, there were 4 (14.8%) missed neoplastic lesions and 15 (30.6%) missed non-neoplastic lesions. One missed lesion was not examined. An AI-based detection system found all the lesions. According to the power analysis, the estimated sensitivity was above 95% (with an alpha value of 0.05 and a power of 80%).
Table: Pathology distribution of colonic lesions with the missing rate of each class
| Pathology | All Lesions | Lesions Missed by Endoscopist | Missing Rate |
| Neoplastic | 27 | 4 | 14.8% |
| Non-Neoplastic | 49 | 15 | 30.6% |
| Not Available | 5 | 1 | 20.0% |
| Total | 81 | 20 | 24.7% |
Conclusion
Optical diagnosis using computer-aided diagnostic systems has a potential role in predicting the presence of polyps. The diagnostic performance of CAD seems to be better than that of endoscopy for colon polyp detection. With recent breakthroughs in artificial intelligence, interest in CAD is gaining traction as a novel approach to improving the quality of colonoscopies. We anticipate that each of these hurdles will likely be overcome during the next several years, which will open the door to the clinical application of CAD in colonoscopy. Large-scale use of this technology can be useful in community practice and service.
References
1. Katrevula A, Katukuri GR, Singh AP, Inavolu P, Rughwani H, Alla SR, Ramchandani M, Duvvur NR. Real-World Experience of AI-Assisted Endocytoscopy Using EndoBRAIN—An Observational Study from a Tertiary Care Center. Journal of Digestive Endoscopy. 2022 Dec 23.
2. Kudo SE, Misawa M, Mori Y, et al. Artificial intelligence-assisted system improves endoscopic identification of colorectal neoplasms. Clin Gastroenterol Hepatol 2020;18(08):1874–1881.e2