Integration of Endocuff‐Assisted and Computer‐Aided Colonoscopy: A Meta‐Analysis of Randomized Controlled Trials
Umar Akram, Eeshal Fatima, Shahzaib Ahmed, Eeman Ahmad, Hareesha Rishab Bharadwaj, Muhammad Hassan Ahmad, Muhammad Sohaib, Khabab Abbasher Hussien Mohamed Ahmed, Dushyant Singh Dahiya

TL;DR
Combining AI-assisted colonoscopy with a device called EndoCuff improves detection of colorectal cancer lesions compared to standard methods.
Contribution
This study is the first meta-analysis to evaluate the combined use of AI and EndoCuff in colonoscopy for lesion detection.
Findings
CADe+EAC significantly improved adenoma detection rate compared to standard colonoscopy.
The combination also improved detection of advanced adenomas and sessile serrated lesions.
CADe+EAC reduced withdrawal and insertion times compared to other methods.
Abstract
Colorectal cancer (CRC) is a leading cause of cancer‐related deaths, with missed lesions during colonoscopy contributing to increased mortality. AI‐assisted computer‐aided detection (CADe) systems help reduce adenoma miss rates, and their integration with mucosal exposure devices like EndoCuff vision may enhance detection. This meta‐analysis assesses the effectiveness of combining CADe and EndoCuff vision‐assisted colonoscopy (EAC) compared to standard colonoscopy or CADe alone. A comprehensive literature search was conducted in MEDLINE, Embase, and clinicaltrials.gov up to November 2024. Only randomized controlled trials (RCTs) comparing CADe+EAC with CADe alone or standard colonoscopy and reporting adenoma detection rate (ADR) were included. Statistical analysis was performed using R version 4.4.0, with mean differences (MDs) and risk ratios (RRs) reported with 95% confidence…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsColorectal Cancer Screening and Detection · COVID-19 diagnosis using AI · AI in cancer detection
