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

Computer-aided polyp detection (CADe) systems based on artificial intelligence are increasingly used in routine endoscopic colorectal cancer screening. These systems are regularly updated and occasionally provide adjustable detection thresholds that impact their performance. However, little is known about the effect of these adjustments on the system’s performance.

Aims & Methods

The objective of this study is to evaluate the performance of various CADe systems on a common benchmark dataset, and to compare the performance of different versions and configurations of the same system. The study employed a total of 101 colonoscopy videos, and each video containing visible polyps was manually annotated with a bounding box, resulting in a total of 129 705 polyp images. The performance of three different CADe systems was assessed: two versions of GI Genius, two detection types of EndoAID, and the freely available CADe system EndoMind. The evaluation included a comprehensive analysis of sensitivity, false positive rate, and time to first polyp detection. In addition, the quality of the bounding boxes in delimiting the polyp was also evaluated by calculating the intersection over the union.

Results

EndoAID (type A), the earlier version of GI Genius, and EndoMind detected all 93 polyps in at least one image. Both the later version of GI Genius and EndoAID (type B) missed one polyp. The mean per-frame sensitivity for each system was 50.63 % for the earlier version of GI Genius, 67.85 % for the later version of GI Genius, 65.60 % for EndoAID (type A), 52.95 % for EndoAID (type B), and 60. 22 % for EndoMind. Regarding the mean first detection time, the earlier version of GI Genius required 1 510 ms, the later version of GI Genius required 607 ms, EndoAID (type A) required 659 ms, EndoAID (type B) required 1 316 ms, and EndoMind required 1 083 ms. The CADe system that presented the fewest number of false positive frames was EndoAID (Type B) with a ratio of only 0.63 %. Lastly, EndoMind was the best performing system in terms of bounding box quality with an intersection over union value of 68.32 %.

Conclusion

This study provides a direct comparison of the performance of different CADe systems, as well as different versions and configurations of the same system. The results show that the performance of CADe systems can vary significantly depending on the system used, the version of the system, and the configuration of the system. By providing an objective evaluation of the strengths and weaknesses of CADe systems, this study can help clinicians and researchers select the most appropriate system for their specific needs.

Introduction

Computer-Aided Diagnosis (CADx) is emerging as a tool to assist endoscopists in differentiating neoplastic from non-neoplastic small polyps during colonoscopy. In clinical practice, the application of optical diagnosis strategies, such as ‘’the resect and discard’’ and the ‘’leave-in-situ’’, vary according to the polyp location. This study aimed to evaluate the impact of polyp location (proximal colon vs. distal colon) on the diagnostic performance of CADx systems for small polyps (<5mm) identified during colonoscopy.

Aims & Methods

A comprehensive literature search was conducted in MEDLINE, Scopus, and Embase databases, with the last search performed on March 12, 2024. Studies evaluating the stand-alone performance of real-time CADx for predicting the histology of colorectal polyps ≤5mm in size were included. Studies without pathological confirmation or with fewer than 50 patients were excluded. The primary endpoints were the sensitivity and specificity of the CADx in the proximal and distal colon. Secondary outcomes were the negative predictive value (NPV), positive predictive value (PPV) and accuracy of the CADx alone in the proximal and distal colon. The distal colon comprised the rectum and sigmoid, while the proximal colon comprised the portion extending from the descending colon to the cecum.

Results

We included 11 studies for analysis with a total of 7,782 polyps (<5mm). CADx accuracy was significantly lower in the proximal colon compared to the rectosigmoid in terms of specificity (62% versus 84%; risk ratio (RR): 0.78 [95% confidence interval (CI): 0.72-0.84], I²=47.08%), NPV (64% versus 93%; RR: 0.71 [95% CI: 0.64-0.79], I²=74.69%), and accuracy (81% vs 86%; RR: 0.95 [95% CI: 0.91-0.99], I²=72.50%). On the other hand, sensitivity (87% vs 87%; RR: 1.00 [95% CI: 0.98-1.02], I²=0%) and PPV (87% vs 76%; RR: 1.11 [95% CI: 1.06-1.17], I²=53.85%) were comparable.

Conclusion

The diagnostic performance of CADx appears to depend on the location of the polyp, being substantially lower for proximal than distal lesions, mainly due to a lower specificity in the proximal colon.

Disclosure

Cesare Hassan: Fujifilm Co. (consultancy); Medtronic Co. (consultancy), Yuichi Mori: Olympus Corp (consultancy, speaking honorarium, equipment loan); Cybernet System (ownership interest), Alessandro Repici: Fujifilm Co. (consultancy); Olympus Corp (consultancy); Medtronic Co. (consultancy).