ArticlePloS one2020
Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Artificial intelligence for reducing missed detection of adenomas and polyps in colonoscopy: A systematic review and meta-analysis.World journal of gastroenterology · 2025Pooled it
- Efficacy of international web-based educational intervention in the detection of high-risk flat and depressed colorectal lesions higher (CATCH project) with a video: Randomized trial.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2022Trial
- Advanced computer-aided detection system exhibits no more false positives than experienced endoscopists in an image-based comparative study of colon polyps.Frontiers in medicine · 2026Article
- Endostrat: a multicenter study of AI-assisted two-level risk stratification for gastric precancerous and early neoplastic lesion screening.Frontiers in medicine · 2026Article
- Eye Tracking Analysis to Determine the Endoscopist's Recognition Rate for Artificial Intelligence-Detected Sites in Colonoscopy.Digestive diseases and sciences · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- A deep neural network improves endoscopic detection of laterally spreading tumors.Surgical endoscopy · 2025Article
- Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.Frontiers in medicine · 2025Article
- Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study.Frontiers in oncology · 2024Article
- Development of an artificial intelligence tool for detecting colorectal lesions in inflammatory bowel disease.iGIE : innovation, investigation and insights · 2023Article
- Review
- A systematic review on application of deep learning in digestive system image processing.The Visual computer · 2023Review
- Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial.Gastroenterology report · 2023Article
- Review
- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
- Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.Scientific reports · 2022Article
- Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device.Endoscopy international open · 2021Article
- Pathological Analysis and Endoscopic Characteristics of Colorectal Laterally Spreading Tumors.Cancer management and research · 2021Article
- The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study.Therapeutic advances in gastroenterology · 2020Article
- Serrated polyp detection rate in colonoscopies performed by gastrointestinal fellows.Therapeutic advances in gastrointestinal endoscopyArticle
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEvidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy.
aimThe detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated.
methodsA previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated.
results(1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively.
conclusionsThis study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.
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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.