Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis.
The emergence of artificial intelligence (AI) has greatly promoted the development of the field of medical image analysis, but the potential benefits of AI-assisted colonoscopy and diagnosis (CADe/CADx) for the detection rate of colorectal adenomas and the histological diagnosis of polyps are still controversial and unknown.
We conducted a search on PubMed, Web of Science, Embase, and Cochrane, and the last search time was August 2024. We collected adenoma detection rate (ADR), polyp detection rate (PDR), and sessile serrated lesion detection rate (SSL). Paired analysis and network meta-analysis (NMA) were performed using R Studio. StataSE15.0 software was used for statistical analysis to calculate the sensitivity and specificity of CADx and conventional colonoscopy.
We included a total of 64 studies, including 52 RCT studies and 12 clinical studies, with a total of 50,834 patients undergoing colonoscopy. The results showed that different adjuvant interventions had significant differences in the detection rate of adenoma compared with routine colonoscopy ADR [RR = 1.20, 95% CI (1.14, 1.26), P < 0.001], and the results were statistically significant. Among different CADe models and advanced optical imaging techniques, ENDOANGEL model-assisted colonoscopy is the most effective method for detecting colorectal adenomas and polyps (97.8%), and Endocuff-AI model-assisted colonoscopy is the most effective method for detecting sessile serrated lesions (94.4%). In the performance study of endoscopists with or without CADX-assisted diagnosis, the optical diagnostic sensitivity of colorectal adenomas was (88% VS 86%), specificity (78% VS 77%), and AUC area (91% VS 89%), and the study results showed no significant differences.
ENDOANGEL model-assisted colonoscopy shows the best efficacy on both ADR and PDR, Endocuff-AI model-assisted colonoscopy shows the best performance on SSL, and compared with optical evaluation without CADx, real-time polyp assessment using CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during colonoscopy.
We conducted a search on PubMed, Web of Science, Embase, and Cochrane, and the last search time was August 2024. We collected adenoma detection rate (ADR), polyp detection rate (PDR), and sessile serrated lesion detection rate (SSL). Paired analysis and network meta-analysis (NMA) were performed using R Studio. StataSE15.0 software was used for statistical analysis to calculate the sensitivity and specificity of CADx and conventional colonoscopy.
We included a total of 64 studies, including 52 RCT studies and 12 clinical studies, with a total of 50,834 patients undergoing colonoscopy. The results showed that different adjuvant interventions had significant differences in the detection rate of adenoma compared with routine colonoscopy ADR [RR = 1.20, 95% CI (1.14, 1.26), P < 0.001], and the results were statistically significant. Among different CADe models and advanced optical imaging techniques, ENDOANGEL model-assisted colonoscopy is the most effective method for detecting colorectal adenomas and polyps (97.8%), and Endocuff-AI model-assisted colonoscopy is the most effective method for detecting sessile serrated lesions (94.4%). In the performance study of endoscopists with or without CADX-assisted diagnosis, the optical diagnostic sensitivity of colorectal adenomas was (88% VS 86%), specificity (78% VS 77%), and AUC area (91% VS 89%), and the study results showed no significant differences.
ENDOANGEL model-assisted colonoscopy shows the best efficacy on both ADR and PDR, Endocuff-AI model-assisted colonoscopy shows the best performance on SSL, and compared with optical evaluation without CADx, real-time polyp assessment using CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during colonoscopy.
Authors
Tan Tan, Zeng Zeng, Liu Liu, Yang Yang, Chen Chen, Zhang Zhang, Li Li, Liu Liu, Li Li, Xu Xu
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