000168500 001__ 168500
000168500 005__ 20260209162330.0
000168500 0247_ $$2doi$$a10.1038/s41746-025-02270-1
000168500 0248_ $$2sideral$$a147926
000168500 037__ $$aART-2026-147926
000168500 041__ $$aeng
000168500 100__ $$aKader, Rawen
000168500 245__ $$aA novel cloud-based artificial intelligence for real-time detection of colorectal neoplasia – a randomized controlled trial (EAGLE)
000168500 260__ $$c2026
000168500 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168500 5203_ $$aPreviously, colorectal polyp computer-aided detection (CADe) systems required on-site high-performance hardware installations (e.g., FPGAs/GPUs), creating practical challenges to upgrades and tying hospitals to legacy hardware. Cloud-based CADe solutions overcome these constraints. Hospitals can use low-specification/low-cost hardware to stream data to the cloud for analysis, enabling frequent AI hardware and algorithm updates. Furthermore, existing CADe systems’ benefits are largely limited to smaller, less clinically relevant polyps ( < 10 mm). This parallel-group RCT evaluated a real-time cloud-deployed CADe-system trained on an enhanced dataset of clinically significant polyps (large polyps( ≥ 10 mm) and sessile-serrated-lesions(SSLs)). Patients from eight centers across four European countries (841 patients, 22 endoscopists) were randomized to standard or CADe-assisted colonoscopy. Co-primary endpoints were (1) superior Adenomas Per-Colonoscopy (APC), (2) non-inferior Positive Percent-Agreement (PPA) (proportion of resections confirmed as clinically relevant polyps). CADe improved (p < 0.05): APC (0.82 vs. 0.62, Ratio 1.33[95% CI 1.06–1.67]), adenoma detection-rate (43.2% vs. 35.9%), SSL (0.08 vs. 0.03, Ratio 3.30[95% CI 1.41–7.57]), and large polyp (0.12 vs. 0.05, Ratio 2.36[95% CI 1.33–4.17]) detection. PPA was non-inferior, and average cloud-network latency was 59.4 ms per minute, with 99.6% under the 100 ms threshold required for real-time use. This RCT demonstrates the feasibility and efficacy of a real-time cloud-based CADe system, with promising outcomes for clinically significant polyps (large polyps and SSLs). Future research should explore optimizing CADe systems' performance. ClinicalTrials.gov (NCT05730192[15/02/2023]).
000168500 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168500 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168500 700__ $$aHassan, Cesare
000168500 700__ $$0(orcid)0000-0001-5932-2889$$aLanas, Ángel$$uUniversidad de Zaragoza
000168500 700__ $$aRomanczyk, Marcin
000168500 700__ $$aRomanczyk, Tomasz
000168500 700__ $$aKotowski, Bronislaw
000168500 700__ $$aHomedes, Carlos Sostres
000168500 700__ $$aMangiavillano, Benedetto
000168500 700__ $$aBonanno, Giacomo
000168500 700__ $$aLovat, Laurence B.
000168500 700__ $$aKaminski, Michal
000168500 700__ $$aFaiss, Siegbert
000168500 700__ $$aRepici, Alessandro
000168500 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000168500 773__ $$g9, 84 (2026), [10 pp.]$$pnpj digit. med.$$tnpj digital medicine$$x2398-6352
000168500 8564_ $$s1143876$$uhttps://zaguan.unizar.es/record/168500/files/texto_completo.pdf$$yVersión publicada
000168500 8564_ $$s2582583$$uhttps://zaguan.unizar.es/record/168500/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168500 909CO $$ooai:zaguan.unizar.es:168500$$particulos$$pdriver
000168500 951__ $$a2026-02-09-14:42:14
000168500 980__ $$aARTICLE