Trial reportEndoscopy2023
Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists.
Trial report in Endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
What it found
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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
28 citing papers in PubMed, 4 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis.International journal of colorectal disease · 2025Pooled it
- Computer-aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta-analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Pooled it
- Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023.Frontiers in oncology · 2024Pooled it
- Prospective Video-Based Study Assessing Effect of Computer-Assisted Optical Diagnosis on Distinguishing Serrated, Hyperplastic, and Adenomatous Colorectal Polyps.Digestive diseases and sciences · 2025Trial
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.Global health & medicine · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Structured Integration of an Artificial Intelligence-Based System for the Optical Diagnosis of Colorectal Polyps.Gut and liver · 2026Article
- Computer-aided diagnosis system for predicting deeply invasive colorectal cancer: a comparison with a prospective study using magnifying chromoendoscopy.Surgical endoscopy · 2026Article
- [Risks and benefits of artificial intelligence in luminal endoscopy].Innere Medizin (Heidelberg, Germany) · 2026Review
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Asia-Pacific consensus on the use of artificial intelligence in colorectal cancer screening and surveillance.iGIE : innovation, investigation and insights · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Review
- Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.Endoscopy · 2025Article
- [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- PolyDeep Advance 1: Clinical Validation of a Computer-Aided Detection System for Colorectal Polyp Detection with a Second Observer Design.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundWe aimed to compare the accuracy of the optical diagnosis of diminutive colorectal polyps, including sessile serrated lesions (SSLs), between a computer-aided diagnosis (CADx) system and endoscopists during real-time colonoscopy.
methodsWe developed the POLyp Artificial Recognition (POLAR) system, which was capable of performing real-time characterization of diminutive colorectal polyps. For pretraining, the Microsoft-COCO dataset with over 300 000 nonpolyp object images was used. For training, eight hospitals prospectively collected 2637 annotated images from 1339 polyps (i. e. publicly available online POLAR database). For clinical validation, POLAR was tested during colonoscopy in patients with a positive fecal immunochemical test (FIT), and compared with the performance of 20 endoscopists from eight hospitals. Endoscopists were blinded to the POLAR output. Primary outcome was the comparison of accuracy of the optical diagnosis of diminutive colorectal polyps between POLAR and endoscopists (neoplastic [adenomas and SSLs] versus non-neoplastic [hyperplastic polyps]). Histopathology served as the reference standard.
resultsDuring clinical validation, 423 diminutive polyps detected in 194 FIT-positive individuals were included for analysis (300 adenomas, 41 SSLs, 82 hyperplastic polyps). POLAR distinguished neoplastic from non-neoplastic lesions with 79 % accuracy, 89 % sensitivity, and 38 % specificity. The endoscopists achieved 83 % accuracy, 92 % sensitivity, and 44 % specificity. The optical diagnosis accuracy between POLAR and endoscopists was not significantly different (
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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.