Trial reportUnited European gastroenterology journal2023
Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).
Trial report in United European gastroenterology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 4 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- The role of artificial intelligence in adenoma detection during colonoscopy: a systematic review and meta-analysis of randomized controlled trials-Artificial intelligence and adenoma detection.Techniques in coloproctology · 2026Pooled it
- Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A Systematic Review and Network Meta-Analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Pooled it
- Effect of AI-assisted diagnosis on adenomas of different sizes: a meta-analysis with evidence from RCTs and trial sequential analysis.BMC gastroenterology · 2025Pooled it
- Optimizing surveillance in Lynch syndrome: lesion detection and comparative performance of different colonoscopy modalities-a systematic review and network meta-analysis.International journal of colorectal disease · 2025Pooled it
- Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).United European gastroenterology journal · 2023Trial
- Comments on "Artificial Intelligence in Colonoscopy Surveillance for Lynch Syndrome: Emerging Evidence, Lessons Learned From Average-Risk Populations, and Future Directions".International journal of cancer · 2026Article
- Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.International journal of cancer · 2026Review
- Computer-aided polyp detection and characterisation systems to support colonoscopy: a systematic review with results stratified by each individual artificial intelligence system.BMJ digital health & AI · 2026Article
- Contemporary Surgical Management of Colorectal Cancer in Lynch Syndrome: Considering the Implications of Known Genotype.ANZ journal of surgery · 2025Review
- An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.United European gastroenterology journal · 2025Article
- Lynch syndrome and colorectal cancer: A review of current perspectives in molecular genetics and clinical strategies.Oncology research · 2025Review
- Colorectal cancers associated with mismatch repair deficiency.Frontiers in medicine · 2025Review
- Concordance with CONSORT-AI guidelines in reporting of randomised controlled trials investigating artificial intelligence in oncology: a systematic review.BMJ oncology · 2025Article
- Review
- Artificial intelligence for cancer screening and surveillance.ESMO real world data and digital oncology · 2024Review
- Incidences of colorectal adenomas and cancers under colonoscopy surveillance suggest an accelerated "Big Bang" pathway to CRC in three of the four Lynch syndromes.Hereditary cancer in clinical practice · 2024Article
- UEG Journal's impact and vision for the future.United European gastroenterology journal · 2024Article
- Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis.Digestive diseases and sciences · 2024Article
- Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.Cancer medicine · 2024Observational
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLynch syndrome (LS), an autosomal dominant disorder caused by pathogenic germline variants in DNA mismatch repair (MMR) genes, represents the most common hereditary colorectal cancer (CRC) syndrome. Lynch syndrome patients are at high risk of CRC despite regular endoscopic surveillance.
objectiveOur aim was to investigate the diagnostic performance of artificial intelligence (AI)-assisted colonoscopy in comparison to High-Definition white-light endoscopy (HD-WLE) for the first time.
methodsPatients ≥18 years with LS, with a pathogenic germline variant (MLH1, MHS2, MSH6), and at least one previous colonoscopy (interval 10-36 months) were eligible. Patients were stratified by previous CRC and affected MMR gene with a 1:1 allocation ratio (AI-assisted vs. HD white-light endoscopy) in this exploratory pilot trial.
resultsBetween Dec-2021 and Dec-2022, 101 LS patients were randomised and 96 patients were finally analyzed after exclusion of 5 patients due to insufficient bowel preparation. In the HD-WLE arm, adenomas were detected in 12/46 patients compared to 18/50 in the AI arm (26.1% [95% CI 14.3-41.1] vs. 36.0% [22.9-50.8]; p = 0.379). The use of AI-assisted colonoscopy especially increased detection of flat adenomas (Paris classification 0-IIb) (examinations with detected flat adenomas: 3/46 [6.5%] vs. 10/50 [20%]; p = 0.07; numbers of detected flat adenomas: 4/20 vs. 17/30, p = 0.018). The median withdrawal time did not differ significantly between HD-WLE and AI (14 vs. 15 min; p = 0.170).
conclusionWe here present first data suggesting that real-time AI-assisted colonoscopy is a promising approach to optimize endoscopic surveillance in LS patients, in particular to improve the detection of flat adenomas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.