Trial reportScientific reports2024
A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection.
Trial report in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Clinical Efficacy of Real-Time Artificial Intelligence-Assisted Colonoscopy in Colorectal Polyp Detection: A Prospective Multicenter Randomized Controlled Trial.Gut and liver · 2026Trial
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- AI-Supported Prediction of Vesicoureteral Reflux in Children Based on Cystoscopic Configuration of the Ureteric Orifice.Journal of clinical medicine · 2026Article
- Availability and use of computer-aided detection during colonoscopy: A real-world observational study at an Australian tertiary center.World journal of gastroenterology · 2026Observational
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Artificial Intelligence in Colonoscopy: A Systematic Review of Adenoma Versus Polyp Detection Rates.Cureus · 2025Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Optical Imaging Technologies and Clinical Applications in Gastrointestinal Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
- Artificial intelligence alert system based on intraluminal view for colonoscopy intubation.Scientific reports · 2025Article
- Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.Scientific reports · 2025Article
- A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management.Cureus · 2025Review
- Advances in colorectal cancer screening: technological innovations, guideline discrepancies, and individualized strategies.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. To develop the AI-assisted program, the dataset was fully anonymized and divided into 10 folds for 10-fold cross-validation. Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. The final model was developed as a real-time polyp-detection program for endoscopy. To evaluate the model's performance, a prospective randomized controlled trial was conducted at six institutions to compare the polyp detection rates (PDR). A total of 805 patients were included. The group that utilized the AI model showed significantly higher PDR and adenoma detection rate (ADR) than the group that underwent colonoscopy without AI assistance. Multivariate analysis revealed an OR of 1.50 for cases where polyps were detected. The AI-assisted polyp-detection program is clinically beneficial for detecting polyps during colonoscopy. By utilizing this AI-assisted program, clinicians can improve adenoma detection rates, ultimately leading to enhanced cancer prevention.
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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.