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EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE FOR COLONOSCOPY ON ADENOMA AND POLYP MISS RATE: A META-ANALYSIS OF TANDEM RCTS

Marcello Fabio Maida 1, Giovanni Marasco 2,3, Marco Spadaccini 4,5, Antonio Facciorusso 6, Emanuele Sinagra 7, Cesare Hassan 4,5

Affiliations

1 S. Elia-Raimondi Hospital, Caltanissetta, Italy

2 IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy

3 Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

4 Humanitas Clinical and Research Hospital, Endoscopy Unit, Rozzano, Italy

5 Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy, Rozzano, Italy

6 Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy

7 Gemelli Giglio Institute-Foundation, Cefalù, Italy

Summary

AI Generated

Meta-analysis of 4 randomized controlled trials (1166 patients) demonstrates that AI-assisted colonoscopy with computer-aided polyp detection significantly reduces adenoma and polyp miss rates compared to white light colonoscopy in tandem-design studies.

Pooled adenoma miss rate was significantly lower with CAD-first versus white light-first colonoscopy (15.5% versus 35.0%; RR 0.45, 95% CI 0.35-0.58, P<0.001).
Polyp miss rate was also significantly lower with CAD-first versus white light-first colonoscopy (16.0% versus 38.1%; RR 0.43, 95% CI 0.30-0.61, P<0.001).
No significant difference was observed in advanced adenoma miss rate (8.7% versus 15.6%, P=0.71) or sessile serrated lesion miss rate (11.6% versus 40.3%, P=0.13), though the authors note these findings require confirmation in larger studies.
The authors conclude that CADe has potential implications for reducing interval colorectal cancer incidence, though this represents their interpretation rather than a direct study outcome.

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This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). Colonoscopy assisted by artificial intelligence (AI) with computer-aided polyp detection (CADe) showed higher adenoma detection rate (ADR) and higher adenoma per colonoscopy compared to white light (WL) colonoscopy in randomized clinical trials (RCTs). Despite ADR is a reliable quality indicator and has been associated to the risk of I-CRC and death, an adequate ADR does not eliminate the risk of lesion miss rate since the relationship between ADR and adenoma miss rate (AMR) is only indirect.

Aims & Methods

The aim of this meta-analysis is to summarise the performance of CADe systems on miss rate of colonic lesions. PubMed/Medline, Embase and Cochrane Library were systematically searched through October 2022 by two independent reviewers (M.M. and A.F.) for tandem-design RCTs comparing miss rate of colonic lesions in CAD-first colonoscopy followed by WL colonoscopy (experimental arm) vs WL-first colonoscopy followed by CAD colonoscopy (control arm).
The primary outcomes were pooled AMR and polyp miss rate (PMR). Secondary outcomes were advanced adenoma miss rate (aAMR) and sessile serrated lesion miss rate (SMR). Lesion miss rate was defined as the number of lesions detected at second colonoscopy divided by the total number of same lesions detected at first and second colonoscopy. A random-effect model was applied for pooling results. Heterogeneity was expressed as I².

Results

Overall, 4 RCTs (1166 patients), were included in the analysis. Pooled AMR was significantly lower in the CAD-first compared to WL-first arm (131/842, 15.5% vs 300/857, 35.0%; RR, 0.45; 95% CI, 0.35-0.58; P<0.001; I²=49%). PMR was also lower in the CAD-first compared to WL-first arm (192/1198, 16.0% vs 474/1244, 38.1%; RR, 0.43; 95% CI, 0.30-0.61; P<0.001; I²=82%).
No significant difference in aAMR (2/23, 8.7% vs 5/32, 15.6%; RR, 1.28; 95% CI, 0.34-4.83; P=0.71; I²=0%) and in SMR (5/43, 11.6% vs 27/67, 40.3%; RR, 0.44; 95% CI, 0.15-1.28; P=0.13; I²=46%) were observed.

Table 1. Characteristics of the studies included in the meta-analysis.

Study	Country	Study type	Design	CADe system	Colonoscopy indication	Arm (subjects, n)	APC (mean)	AMR	aAMR	PMR
Wang P, 2020	China	RCT	Tandem	EndoScreener, Wision AI, Shanghai, China	Symptomatic: 224 Screening or surveillance: 145	CADe-first (184)	0.78	13.9%	50.0%	13.0%
						WLE-first (185)	0.65	40.0%	25.0%	45.9%
Kamba S, 2021	Japan	RCT	Tandem	YOLOv3, LPIXEL Inc. and Jikei University School of Medicine	Symptomatic: 0 Screening or surveillance: 344	CADe-first (171)	1.42	13.8%	0.0%	14.2%
						WLE-first (173)	1.25	36.7%	13.3%	40.6%
Glissen JR, 2022	US	RCT	Tandem	EndoScreener, Wision AI, Shanghai, China	Symptomatic: 0 Screening or surveillance: 223	CADe-first (113)	1.19	20.1%	11.1%	20.7%
						WLE-first (110)	0.90	31.2%	0.0%	33.7%
Wallace M, 2022	US	RCT	Tandem	GI-Genius; Medtronic, Minneapolis, US	Symptomatic: 0 Screening or surveillance: 230	CADe-first (116)	1.79	15.7%	-	16.8%
						WLE-first (114)	1.46	32.2%	-	31.1%

Conclusion

According to available evidence, the use of colonoscopy assisted by AI with CADe results in a significant lower AMR and PMR, with a potential implication in reducing the incidence of I-CRC. No significant difference in aAMR and SMR has been observed, even if these results should be further confirmed in studies with larger sample sizes.

Conference

UEG Week Copenhagen 2023

Topics

Colorectal

Submission format

Abstract

Session

PP 06 Other lower GI disorders (Posters)

Citation

United European Gastroenterology Journal 2023; 11 (Supplement 8)

Published

2023

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DISTINCT TISSUE AND BLOOD PROTEOMIC SIGNATURES ARE ASSOCIATED WITH FIBROSTENOTIC CROHN’S DISEASE

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DISTINCT TISSUE AND BLOOD PROTEOMIC SIGNATURES ARE ASSOCIATED WITH FIBROSTENOTIC CROHN’S DISEASE

Anja Poulsen 1,2, Simon Lizarazo 3, Maria Am Pedersen 1,2, Lene Buhl Riis 4,2, Cathy Lu 5, Julie Rasmussen 6, Ismail Gögenur 7,2, Johan Burisch 8,2, Kate Paget 9, Jacob Seidelin 4,2, Swati Venkat 3, Darren Ruane 9, Florian Rieder 10

Affiliations

1 Rigshospitalet, Copenhagen Ø, Denmark

2 University of Copenhagen, Copenhagen, Denmark

3 Johnson & Johnson Innovative Medicine, Springhouse, United States

4 Herlev Hospital, Herlev, Denmark

5 University Of Calgary, Calgary, Canada

6 Køge Hospital, Køge, Denmark

7 Zealand University Hospital, Køge, Denmark

8 Hvidovre University Hospital, Hvidovre, Denmark

9 Johnson & Johnson Innovative Medicine, La Jolla, United States

10 Cleveland Clinic Foundation, Cleveland, United States