Cloud-Based AI System Shows Promise in Detecting High-Risk Colorectal Lesions, Study Finds

The EAGLE trial, a multicenter randomized controlled study published in npj Digital Medicine, indicates that cloud-deployed artificial intelligence can help endoscopists detect lesions most critical for preventing progression to colorectal cancer. The study evaluated the CADDIE device, the first cloud-based Computer-Aided Detection application for real-time polyp detection during colonoscopy that is both FDA-cleared and CE-marked.

Conducted across eight centers in four European countries, the trial involved 841 patients and 22 endoscopists performing screening and surveillance colonoscopies. Patients were randomized to standard colonoscopy or CADDIE-assisted colonoscopy. The primary analysis revealed that use of the CADDIE application was associated with a 7.3% absolute increase in adenoma detection rate compared to standard colonoscopy.

Significant relative increases in lesions detected per colonoscopy were observed for clinically relevant lesion subtypes: 93% for large adenomas (>10 mm), 57% for non-polypoid adenomas, and 230% for sessile serrated lesions (SSLs). SSLs are high-risk lesions whose detection is critical to reducing the risk of post-colonoscopy colorectal cancer, according to research published in Gastrointestinal Endoscopy and The Lancet Gastroenterology & Hepatology.

The CADDIE application is trained on a dataset enriched in clinically relevant and hard-to-detect lesions, including flat sessile serrated lesions and large polyps. Lesions with sessile or flat morphology are difficult to detect and can harbor clinically relevant pathology. The ability to reliably detect SSLs is increasingly viewed as a critical quality consideration in colonoscopy, as noted in quality indicator guidelines published in Gastrointestinal Endoscopy.

The system demonstrated real-time performance and operational efficiency across diverse testing environments. 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.

‘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.’

For complete access to the EAGLE Trial study, visit https://doi.org/10.1038/s41746-025-02270-1. The gastroenterologist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp based on their own medical judgment. 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.

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