New study results show Olympus Corporation’s CADDIE artificial intelligence (AI) solution helps detect high-risk and hard-to-detect colorectal lesions.

Olympus derived the data from the EAGLE Trial,¹ a multicenter randomized controlled study evaluating the company’s CADDIE device, the first cloud-based computer-aided detection (CADe) application for real-time polyp detection during a colonoscopy that is authorized in both the United States and Europe. The CADDIE software is the first solution introduced as part of Olympus’ OLYSENSE Intelligent Endoscopy Ecosystem.

“The publication of EAGLE study is a pivotal moment for Olympus, supporting clinical adoption of the CADDIE device as an AI solution that can enhance detection of clinically relevant lesions without compromising safety or efficiency,” said Miquel Àngel García, executive vice president and general manager, Gastrointestinal Business Unit, Olympus Corporation.

The trial indicates that cloud-deployed AI can help endoscopists detect the lesions that matter most in preventing progression to cancer^2-5—large adenomas, particularly those flat in morphology, and sessile serrated lesions (SSLs)—without disrupting safety or workflow. The study has been published in npj Digital Medicine.¹

The EAGLE (Evaluation of AI for detection of Gastrointestinal Lesions in Endoscopy) study was conducted across eight centers in four European countries,⁶ and its primary analysis involved 841 patients and 22 endoscopists performing screening and surveillance colonoscopies. Patients were randomized to standard colonoscopy or CADDIE-assisted colonoscopy.

Key Findings

• Improved detection of high-risk and hard-to-detect lesions. In screening and surveillance patients, use of the CADDIE application was associated in this study with 7.3% absolute increase in adenoma detection rate (ADR), compared to standard colonoscopy. Moreover, significant relative increases in lesions detected per colonoscopy were observed in this study for clinically relevant lesion subtypes: 93% for large (>10 mm) adenomas, 57% for non-polyploid adenomas and 230% for SSLs.
• Feasible and efficient real-time cloud-based deployment. The system demonstrated real-time performance and operational efficiency across diverse testing environments.

“This study marks a pivotal shift in the clinical translation of AI-assisted endoscopy,” said Rawen Kader, principal investigator of the EAGLE Trial and GI researcher at University College London. “Cloud deployment can remove hardware barriers and give hospitals access to the latest AI innovations, which has the potential of improving detection of the lesions that matter most for reducing colorectal cancer risk.”

The CADDIE application is trained on a dataset enriched in clinically relevant and hard-to-detect lesions, including flat sessile serrated lesions (SSLs) and large polyps (≥10 mm).

Lesions with sessile or flat morphology are difficult to detect and can harbor clinically relevant pathology. SSLs, in particular, are high-risk lesions whose detection is critical to reducing the risk of post-colonoscopy colorectal cancer.^3-4 The ability to reliably detect SSLs is increasingly viewed as a critical quality consideration in colonoscopy.⁷ This study demonstrates increased detection of clinically relevant lesions and no increase in unnecessary resections, addressing some of the concerns raised in recent guidelines.^8-9  

The CADDIE application leverages a cloud architecture that uses industry standard security controls. Cloud deployment offers hospitals flexibility, reducing reliance on hardware and enabling subscription-based procurement models. This approach can democratize access to advanced AI tools and lays the foundation for future AI applications in endoscopy.

“The EAGLE trial demonstrates how cloud‑based AI can be translated into routine endoscopy at scale,” stated Odin Vision CEO Peter Mountney, vice president of Olympus Corporation’s AI Unit. “By delivering AI in real time through the cloud, we can help accelerate innovation and enable hospitals around the world to benefit from our latest, evidence‑based technologies to support clinicians and enhance the quality of care.”

CADDIE is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of treatment/course of action for patients. The CADDIE computer-assisted detection device is limited for use with standard white-light endoscopy imaging only.

As a global medical technology company, Olympus partners with healthcare professionals to provide solutions and services for early detection, diagnosis and minimally invasive treatment, aiming to improve patient outcomes by elevating the standard of care in targeted disease states. For more than 100 years, Olympus has produced products designed to deliver optimal outcomes for customers.

References
¹ Kader R, Hassan C, Lanas Á, et al. A novel cloud-based artificial intelligence for real-time detection of colorectal neoplasia – a randomized controlled trial (EAGLE). npj Digit.l Med.. Published online December 26, 2025.  https://doi.org/10.1038/s41746-025-02270-1
² Nguyen LH, Goel A, Chung DC. Pathways of Colorectal Carcinogenesis. Gastroenterology. 2020;158(2):291-302. doi:10.1053/j.gastro.2019.08.059
³ Anderson JC, Hisey W, Mackenzie TA, et al. Clinically significant serrated polyp detection rates and risk for postcolonoscopy colorectal cancer: data from the New Hampshire Colonoscopy Registry. Gastrointest Endosc. 2022;96(2):310-317. doi:10.1016/j.gie.2022.03.001
⁴ Toledo DEFWMv, IJspeert JEG, Bossuyt PMM, et al. Serrated polyp detection and risk of interval post-colonoscopy colorectal cancer: a population-based study. Lancet Gastroenterol Hepatol. 2022;7(8):747-754. doi:10.1016/s2468-1253(22)00090-5
⁵ Soetikno RM. Prevalence of Nonpolypoid (Flat and Depressed) Colorectal Neoplasms in Asymptomatic and Symptomatic Adults. JAMA. 2008;299(9):1027. doi:10.1001/jama.299.9.1027
⁶  Italy, Germany, Spain, Poland
⁷ Rex DK, Anderson JC, Butterly LF, et al. Quality indicators for colonoscopy. Gastrointest Endosc. 2024;100(3):352-381. doi:10.1016/j.gie.2024.04.2905
⁸ Bretthauer M, Ahmed J, Antonelli G, et al. Use of computer-assisted detection (CADe) colonoscopy in colorectal cancer screening and surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy. Published online 2025:. doi:10.1055/a-2543-0370
⁹ Sultan S, Shung DL, Kolb JM, et al. AGA Living Clinical Practice Guideline on Computer-Aided Detection–Assisted Colonoscopy. Gastroenterology. 2025;168(4):691-700. doi:10.1053/j.gastro.2025.01.002