Artificial Intelligence–Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial

Xu, Hong; Tang, Raymond; Lam, Thomas Y.; Zhao, Guijun; Lau, James; Liu, Yunpeng; Wu, Qi; Rong, Long; Wang, Xu; Li, Xue; Wong, Sunny H.; Cai, Shanshan; Wang, Jing; Liu, Guanyi; Ma, Tantan; Liu, Xiong; Mak, Joyce Wing Yan; Xu, Hui; Yuan, Peng; Cao, Tingting; Li, Fudong; Ye, Zhenshi; Zhang, Shutian; Sung, Joseph J.Y.

doi:10.1016/j.cgh.2022.07.006

Cited by 85 publications

(50 citation statements)

References 29 publications

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“…3 studies [ 16 , 19 , 26 ] reported that the overall advanced adenomas detection rate in CADe group was not statistically significant when compared with the control group, respectively (146/2032, 7.2% vs 119/2058, 5.8%; OR, 1.26; 95% CI, 0.98–1.62; P > .05), 3 studies had low levels of heterogeneity (I2: 0%) (Fig. 3 d).…”

Section: Resultsmentioning

confidence: 99%

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis

Shao¹,

Yan²,

Liu³

et al. 2022

Medicine

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Background: Colonoscopy can detect colorectal adenomas and reduce the incidence of colorectal cancer, but there are still many missing diagnoses. Artificial intelligence-assisted colonoscopy (AIAC) can effectively reduce the rate of missed diagnosis and improve the detection rate of adenoma, but its clinical application is still unclear. This systematic review and meta-analysis assessed the adenoma missed detection rate (AMR) and the adenoma detection rate (ADR) by artificial colonoscopy. Methods: Conduct a comprehensive literature search using the PubMed, Medline database, Embase, and the Cochrane Library. This meta-analysis followed the direction of the preferred reporting items for systematic reviews and meta-analyses, the preferred reporting item for systematic review and meta-analysis. The random effect model was used for meta-analysis. Results: A total of 12 articles were eventually included in the study. Computer aided detection (CADe) significantly decreased AMR compared with the control group (137/1039, 13.2% vs 304/1054, 28.8%; OR,0.39; 95% CI, 0.26–0.59; P < .05). Similarly, there was statistically significant difference in ADR between the CADe group and control group, respectively (1835/5041, 36.4% vs 1309/4553, 28.7%; OR, 1.54; 95% CI, 1.39–1.71; P < .05). The advanced adenomas missed rate and detection rate in CADe group was not statistically significant when compared with the control group. Conclusions: AIAC can effectively reduce AMR and improve ADR, especially small adenomas. Therefore, this method is worthy of clinical application. However, due to the limitations of the number and quality of the included studies, more in-depth studies are needed in the field of AIAC in the future.

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Section: Resultsmentioning

confidence: 99%

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis

Shao¹,

Yan²,

Liu³

et al. 2022

Medicine

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show abstract

“…AI-assisted colonoscopy has been shown to improve adenoma detection rate (ADR) regardless of the level of expertise of the endoscopists. 3 ADR is used as a quality indicator for colonoscopy, and a higher ADR is associated with a lower risk of PCCRC. 4 However, ADR cannot quantify the quality of the colonoscopy beyond the detection of the first adenoma and does not provide indication of whether additional adenomas are missed.…”

mentioning

confidence: 99%

“…Artificial intelligence (AI)‐assisted colonoscopy is now available for routine clinical use. AI‐assisted colonoscopy has been shown to improve adenoma detection rate (ADR) regardless of the level of expertise of the endoscopists 3 . ADR is used as a quality indicator for colonoscopy, and a higher ADR is associated with a lower risk of PCCRC 4 .…”

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confidence: 99%

Harnessing the potential of artificial intelligence‐assisted colonoscopy to reduce the risk of post‐colonoscopy interval colorectal cancer

Sung

2023

J of Gastro and Hepatol

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“…The first studies using approved AI systems unanimously demonstrated an increase of adenoma detection rate (ADR), number of adenomas per patient as well as a reduction in adenoma and neoplasia miss rate that was mainly achieved due to detection of small and diminutive polyps. [2][3][4][5] In some studies, the high number of additional polyps detected with AI could at least in parts be attributed to a low baseline ADR in the control groups. 6 In their paper, Silvia Pecere and colleagues performed an analysis of published data.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Pouring some water into the wine—Poor performance of endoscopists in artificial intelligence studies

Weigt

2022

UEG Journal

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Artificial Intelligence–Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial

Cited by 85 publications

References 29 publications

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis

Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: A protocol for systematic review and meta-analysis

Harnessing the potential of artificial intelligence‐assisted colonoscopy to reduce the risk of post‐colonoscopy interval colorectal cancer

Pouring some water into the wine—Poor performance of endoscopists in artificial intelligence studies

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