Cloud-based Artificial Intelligence for Detection of Colorectal Neoplasia – A Randomized Controlled Trial (EAGLE Trial)

R Kader; C Hassan; Á Lanas; M Romańczyk; T Romańczyk; B Kotowski; C Sostres Homedes; G Bonanno; M Benedetto; M Kaminski; S Faiss; A Repici

doi:10.1055/s-0045-1805103

Endoscopy, Table of Contents

Endoscopy 2025; 57(S 02): S8-S9
DOI: 10.1055/s-0045-1805103

Abstracts | ESGE Days 2025

Best abstracts

ESGE Presidential Session 03/04/2025, 14:30 – 16:30 Room 117+116

Cloud-based Artificial Intelligence for Detection of Colorectal Neoplasia – A Randomized Controlled Trial (EAGLE Trial)

R Kader

¹University College London, London, United Kingdom

,

C Hassan

²Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy

³Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy

,

Á Lanas

⁴Instituto de Investigación Sanitaria Aragón, Instituto Aragonés de Ciencias de La Salud, Zaragoza, Spain

⁵Hospital Clínico Universitario de Zaragoza, Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain

,

M Romańczyk

⁶Department of Gastroenterology, Academy of Silesia, Katowice, Poland

⁷Endoterapia, H-T. Centrum Medyczne, Tych, Poland

,

T Romańczyk

⁷Endoterapia, H-T. Centrum Medyczne, Tych, Poland

⁶Department of Gastroenterology, Academy of Silesia, Katowice, Poland

,

B Kotowski

⁸The M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Wars, Poland

⁹Polish Foundation of Gastroenterology, Warsaw, Poland

,

C Sostres Homedes

¹⁰Hospital Clinico Universitario Lozano Blesa, Zaragoza, Spain

,

G Bonanno

¹¹Endoscopy Unit, Humanitas Istituto Clinico Catanese, Catania, Italy

,

M Benedetto

¹²Gastrointestinal Endoscopy, Istituto Clinico Mater Domini Casa di Cura Privata SpA, Castellanza, Italy

,

M Kaminski

¹³Maria Skłodowska-Curie National Institute of Oncology, Warszawa, Poland

¹⁴Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland

,

S Faiss

¹⁵Sana Klinikum Lichtenberg, Berlin, Germany

,

A Repici

³Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy

²Endoscopy Unit, Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy

› Author Affiliations

Abstract

Full Text

Aims In recent years, several Randomized Controlled Trials (RCTs) have evaluated the role of artificial intelligence (AI) Computer-Aided Detection (CADe) systems to enhance polyp detection during colonoscopy. However, these studies have focused exclusively on locally deployed hardware-based AI systems, whereas cloud-based systems may improve the accessibility and upgrading of CADe technology. ‘CADDIE’ (Odin Vision, an Olympus company) is a cloud-based CADe system that receives the live endoscopy image stream via the hospital’s internet network, where the AI algorithm processes the images and outputs in real-time overlay bounding boxes on the endoscopy screen to highlight suspected polyps. This prospective RCT aimed to evaluate the efficacy and safety of the novel cloud-based CADe-system (‘CADDIE’) in improving polyp detection.

Methods This international, multi-centre, parallel-group RCT was conducted across eight centres in four European countries. Participating endoscopists had a baseline of>1000 colonoscopy procedures and an adenoma detection rate (ADR) of≥25%. Patients aged≥40 years and at average risk for colorectal cancer (CRC) undergoing screening or surveillance indications for colonoscopy were recruited. Patients were randomised in a 1:1 ratio to standard colonoscopy (SoC) or CADDIE-assisted (CADe-arm) colonoscopy. The co-primary endpoints were superiority in adenomas per colonoscopy (APC) as the efficacy endpoint, and non-inferiority for the Positive Percent Agreement (PPA), defined as the percentage of resections that are histologically confirmed adenomas, SSLs and large (> 10mm) hyperplastic polyps of the proximal colon, as the safety endpoint.

Results Overall, 22 endoscopists and 841 participants were included in the analysis. The CADe-arm significantly increased APC by 33% (0.62 vs. 0.82, Ratio 1.33 [95%CI 1.06-1.67], p=0.01) and ADR (35.9% vs. 43.2%, difference of 7.3% [95%CI 0.7%-13.9%], p=0.03) compared to the SoC-arm. The mean detection of sessile serrated lesions (SSLs) (0.03 vs. 0.08, Ratio 3.30 [95%CI 1.41-7.57], p<0.01), large adenomas (0.04 vs. 0.09 vs; Ratio 1.93 [95%CI 1.03-3.62], p=0.04), and large polyps (0.05 vs. 0.12 vs; Ratio 2.36 [95%CI 1.33-4.17], p<0.01) also significantly improved. A higher proportion of large polyps (27.3% vs. 37.0%) and large adenomas (15.8% vs. 24.2%) were non-polypoid ‘flat’ morphology in the CADe-arm compared to the SoC-arm. Other detection metrics, including neoplastic polyps per colonoscopy (0.66 vs. 0.91; Ratio 1.39 [95%CI 1.12-1.73], p<0.01 and polyps per colonoscopy (1.06 vs. 1.41 (Ratio 1.35 [95%CI 1.13-1.61], p<0.01), were significantly higher in the CADe-arm compared to the SoC-arm. Non-inferiority in PPA was achieved (53.4% vs 53.9%; 0.5% (-5.0%,∞)), with no increase in unnecessary resections.

Conclusions This trial demonstrates the safety and efficacy of a cloud-based CADe-system (CADDIE) to increase endoscopists' polyp detection across multiple metrics, most notably for large adenomas, large polyps, and SSLs.