AI-based tools appear to outperform other methods intended to increase ADRs, including distal attachment devices, dye-based/virtual chromoendoscopy, water-based techniques, and balloon-assisted devices, researchers found in a systematic review and meta-analysis.
“ADR is a very important quality metric. The higher the ADR, the less likely the chance of interval cancer,” first author Muhammad Aziz, MD, co-chief gastroenterology fellow at the University of Toledo (Ohio), told this news organization. Interval cancer refers to colorectal cancer that is diagnosed within 5 years of a patient’s undergoing a negative colonoscopy.
“Numerous interventions have been attempted and researched to see the impact on ADR,” he said. “The new kid on the block – AI-assisted colonoscopy – is a game-changer. I knew that AI was impactful in improving ADR, but I didn’t know it would be the best.”
The study was published online in the Journal of Clinical Gastroenterology.
Analyzing detection rates
Current guidelines set an ADR benchmark of 25% overall, with 30% for men and 20% for women undergoing screening colonoscopy. Every 1% increase in ADR results in a 3% reduction in colorectal cancer, Dr. Aziz and his co-authors write.
Several methods can improve ADR over standard colonoscopy. Computer-aided detection and AI methods, which have emerged in recent years, alert the endoscopist of potential lesions in real time with visual signals.
No direct comparative studies had been conducted, so to make an indirect comparison, Dr. Aziz and colleagues undertook a systematic review and network meta-analysis of 94 randomized controlled trials that included 61,172 patients and 20 different study interventions.
The research team assessed the impact of AI in comparison with other endoscopic methods, using relative risk for proportional outcomes and mean difference for continuous outcomes. About 63% of the colonoscopies were for screening and surveillance, and 37% were diagnostic. The effectiveness was ranked by P-score (the probability of being the best treatment).
Overall, AI had the highest P-score (0.96), signifying the best modality of all interventions for improving ADR, the study authors write. A sensitivity analysis using the fixed effects model did not significantly alter the effect measure.
The network meta-analysis showed significantly higher ADR for AI, compared with autofluorescence imaging (relative risk, 1.33), dye-based chromoendoscopy (RR, 1.22), Endocap (RR, 1.32), Endocuff (RR, 1.19), Endocuff Vision (RR, 1.26), EndoRings (RR, 1.30), flexible spectral imaging color enhancement (RR,1.26), full-spectrum endoscopy (RR, 1.40), high-definition (HD) colonoscopy (RR, 1.41), linked color imaging (1.21), narrow-band imaging (RR, 1.33), water exchange (RR, 1.22), and water immersion (RR, 1.47).
Among 34 studies of colonoscopies for screening or surveillance only, the ADR was significantly improved for linked color imaging (RR, 1.18), I-Scan with contrast and surface enhancement (RR, 1.25), Endocuff (RR, 1.20), Endocuff Vision (RR, 1.13), and water exchange (RR, 1.24), compared with HD colonoscopy. Only one AI study was included in this analysis, because the others had significantly more patients who underwent colonoscopy for diagnostic indications. In this case, AI did not improve ADR, compared with HD colonoscopy (RR, 1.44).
In addition, a significantly improved polyp detection rate (PDR) was noted for AI, compared with autofluorescence imaging (RR, 1.28), Endocap (RR, 1.18), Endocuff Vision (RR, 1.21), EndoRings (RR, 1.30), flexible spectral imaging color enhancement (RR, 1.21), full-spectrum endoscopy (RR, 1.39), HD colonoscopy (RR, 1.34), linked color imaging (RR, 1.19), and narrow-band imaging (RR, 1.21). Again, AI had the highest P-score (RR, 0.93).
Among 17 studies of colonoscopy for screening and surveillance, only one AI study was included for PDR. A significantly higher PDR was noted for AI, compared with HD colonoscopy (RR, 1.33). None of the other interventions improved PDR over HD colonoscopy.
