The Efficacy of AI-Assisted CADe Systems in Colorectal Polyps and Adenomas during Colonoscopies: A Systematic Review

Stella Nwosu *

Hematology and Medical Oncology, Emory University, USA.

Norense Ehimwenma

Kharkiv National Medical University, Ukraine

Victor N. Oboli

Lincoln Medical Center - Weill Cornell Medical College, USA.

Faiza Arslan

Rawalpindi Medical University (RMU), Pakistan.

Khizar Scheeraz Khan

Foundation University Medical College (FUMC), Pakistan.

Akata Abung

College of Medicine, University of Calabar, Nigeria.

Aqsa Latif

King Edward Medical University, Pakistan.

Daniel Nwabueze

Igbinedion University Okada, Nigeria.

Chiugo Okoye

Igbinedion University Okada, Nigeria.

Anusha Thalla

Katuri Medical College, India.

Farzana Rahman

Jalalabad Ragib Rabeya Medical College and Hospital, Bangladesh.

Devdat

Liaquat University of Medical and Health Sciences, Pakistan.

Franca Erhiawarie

University of Benin, Benin City, Nigeria.

Sampada Tiwari

College of Medical Sciences, Nepal.

Salman Yousaf

Services Institute of Medical Sciences (SIMS), Pakistan.

*Author to whom correspondence should be addressed.


Abstract

Colorectal polyps and adenomas are a significant health concern that can lead to colorectal cancer. Detecting polyps and adenomas early is important to remove them in a timely manner, reducing the risk of cancer. Artificial intelligence (AI) could assist in identifying polyps and adenomas more efficiently. This study will investigate the potential benefits of AI technology in detecting colorectal adenomas, using AI-assisted colonoscopy diagnostic systems. The goal is to determine if there are differences in detection rates using AI compared to standard colonoscopy methods.  PRISMA Statement 2020 guidelines were adhered to in this systematic review. Three databases were searched including PubMed/MEDLINE, Embase and Web of Science through February 23, 2023. A combination of the following keywords was used: AI, Artificial, Intelligence, Colorectal, Polyp, Adenoma. The adenoma detection rate (ADR) and polyp detection rate (PDR) were additionally meta-analyzed for proportions, and reported as odds ratio (OR), applying 95% confidence intervals (CI). A total of 9 randomized clinical trials were included in this review, pooling in 6981 participants. The findings were presented systematically, based on the design, technology used, effects on ADR and PDR, and key conclusions. The meta-analytical findings were reported as follows. On applying a random-effects model, the odds ratio (OR) for ADR was 1.532 (95% CI=1.125-2.087), with significant detection favoring CADe systems (P=0.0068). Similarly, when assessing the chance of diagnosing colorectal polyps with CADe systems as an add-on compared to standard colonoscopy alone, the odds ratio (OR) was 1.893 (95% CI=1.687-2.125, P<0.0001). This systematic review found that real-time computer-aided detection during colonoscopy improved the detection of adenomas and polyps. The use of the system is feasible and safe, and future studies should focus on improving colonoscopy quality indicators including the most widely used– ADR. Larger randomized controlled studies are needed to evaluate the impact of this system on adenoma and serrated polyp detection and its effect on endoscopists with lower detection rates.

Keywords: Colorectal polyps, colorectal adenomas, colonoscopies, artificial intelligence, CADe


How to Cite

Nwosu , S., Ehimwenma , N., Oboli, V. N., Arslan , F., Khan , K. S., Abung, A., Latif, A., Nwabueze, D., Okoye, C., Thalla , A., Rahman , F., Devdat, Erhiawarie, F., Tiwari , S., & Yousaf , S. (2023). The Efficacy of AI-Assisted CADe Systems in Colorectal Polyps and Adenomas during Colonoscopies: A Systematic Review. Journal of Advances in Medicine and Medical Research, 35(14), 94–104. https://doi.org/10.9734/jammr/2023/v35i145060

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