Efficacy of Artificial Intelligence in Reducing Miss Rates of Gastrointestinal Adenomas, Polyps, and Sessile Serrated Lesions: A Meta-analysis of Randomized Controlled Trials

Background and Aims This study aims to discern if the utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and polyp miss rate (PMR), compared to standard endoscopy. Methods We performed an extensive search of various databases, encompassing, PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus until January 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks (CNN), gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. Our aim was mainly to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate (SSLMR). Results A total of 7 randomized controlled trials (RCTs) were included in this meta-analysis. Pooled AMR was markedly lower in the AI group as opposed to the non-AI group (pooled RR, 0.46; 95% CI, 0.36–0.59, P<0.001). Likewise, PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR: 0.43; 95% CI, 0.27–0.69, P<0.001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, 0.43; 95% CI, 0.20-0.92, P<0.05) and diminutive adenomas (pooled RR, 0.49; 95% CI, 0.26-0.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, 0.48; 95% CI, 0.17-1.37, P=0.17). The average number of polyps (Hedges’g=-0.486, 95% CI=-0.697 to -0.274, P=0.000) and adenomas (Hedges’g=-0.312, 95% CI=-0.551 to -0.074, P=0.01) detected during the second procedure also favored AI. However, the AI implementation did not lead to a prolonged withdrawal time (P>0.05). Conclusions This meta-analysis suggested that AI technology led to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions (SSL) during endoscopy surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopy procedures. This study aims to discern if the utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and polyp miss rate (PMR), compared to standard endoscopy. We performed an extensive search of various databases, encompassing, PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus until January 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks (CNN), gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. Our aim was mainly to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate (SSLMR). A total of 7 randomized controlled trials (RCTs) were included in this meta-analysis. Pooled AMR was markedly lower in the AI group as opposed to the non-AI group (pooled RR, 0.46; 95% CI, 0.36–0.59, P<0.001). Likewise, PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR: 0.43; 95% CI, 0.27–0.69, P<0.001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, 0.43; 95% CI, 0.20-0.92, P<0.05) and diminutive adenomas (pooled RR, 0.49; 95% CI, 0.26-0.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, 0.48; 95% CI, 0.17-1.37, P=0.17). The average number of polyps (Hedges’g=-0.486, 95% CI=-0.697 to -0.274, P=0.000) and adenomas (Hedges’g=-0.312, 95% CI=-0.551 to -0.074, P=0.01) detected during the second procedure also favored AI. However, the AI implementation did not lead to a prolonged withdrawal time (P>0.05). This meta-analysis suggested that AI technology led to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions (SSL) during endoscopy surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopy procedures.