Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis

Saeed Soleymanjahi; Jack Huebner; Lina Elmansy; Niroop Rajashekar; Nando Lüdtke; Rumzah Paracha; Rachel Thompson; Alyssa A Grimshaw; Farid Foroutan; Shahnaz Sultan; Dennis L Shung

doi:10.7326/ANNALS-24-00981

Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis

Ann Intern Med. 2024 Oct 22. doi: 10.7326/ANNALS-24-00981. Online ahead of print.

Authors

Affiliations

¹ Division of Gastroenterology, Mass General Brigham, Harvard School of Medicine, Boston, Massachusetts (S.Soleymanjahi).
² Department of Medicine, Yale School of Medicine, New Haven, Connecticut (J.H., L.E., N.R., R.P., R.T.).
³ Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (N.L.).
⁴ Cushing/Whitney Medical Library, Yale University, New Haven, Connecticut (A.A.G.).
⁵ McMaster University, Hamilton, Ontario, Canada (F.F.).
⁶ Division of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota (S.Sultan).
⁷ Section of Digestive Diseases, Clinical and Translational Research Accelerator, and Department of Biomedical Informatics and Data Science, Department of Medicine, Yale School of Medicine, New Haven, Connecticut (D.L.S.).

PMID: 39531400
DOI: 10.7326/ANNALS-24-00981

Abstract

Background: Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear.

Purpose: To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy.

Data sources: Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024.

Study selection: Published RCTs comparing CADe-enhanced and conventional colonoscopy.

Data extraction: Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture.

Data synthesis: Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77]).

Limitation: Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates.

Conclusion: Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures.

Primary funding source: None. (PROSPERO: CRD42023422835).

Publication types

Review