AI Polyp Detection in Colonoscopy: What the Research Actually Shows
Software that highlights possible polyps on the live scope view. One of the few areas with a solid stack of randomized trials.
This is the best-evidenced corner of healthcare AI, and it is the one people quote least, which is strange.
The reason it is well evidenced is that the study design is easy. Randomize patients to a colonoscopy with the software on or off, count how many adenomas were found. No modeling assumptions, no retrospective test set, just a trial.
The trials generally show more adenomas found. The open argument is what that means. Finding more small, low-risk polyps costs removal time and follow-up surveillance, and the cancers prevented are years away and hard to attribute. That debate is live in the literature right now, and your endoscopy team probably has an opinion worth hearing before you buy.
What Kind of Evidence This Is
Not every study carries the same weight. This is the mix behind the list below.
What to Measure in Your Own Setting
A published result is somebody else's hospital. These are the numbers worth tracking in yours.
- Adenoma detection rate before and after, per endoscopist. Team averages hide the variation that matters.
- Withdrawal time, since a longer, more careful look is itself a known driver of detection and can confound the result.
- Extra polypectomies and the surveillance load they create over the following three years.
- Whether your lowest detectors improved. Software that only helps people who were already good is a weaker case.
The Studies
Every entry links to the source record. Summaries are the study's own abstract, shortened but not reworded.
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Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.
ObjectiveThe effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR.DesignIn an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonosco...
PMID 30814121 ... doi:10.1136/gutjnl-2018-317500
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Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.
Background & aimsThe benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. However, the ADR by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR lowers the risk of interval colorectal cancers by 3%-6%.
PMID 29928897 ... doi:10.1053/j.gastro.2018.06.037
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Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
BackgroundIn general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable.
PMID 29066576 ... doi:10.1136/gutjnl-2017-314547
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Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.
BackgroundComputer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost.ObjectiveTo evaluate the performance of real-time CAD with endocytoscopes (×520 ultramagnifying colonoscopes providing microvascular and cellular visualization of colorectal polyps after application of t...
PMID 30105375 ... doi:10.7326/m18-0249
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Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis.
Background and aimsOne-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting.
PMID 32598963 ... doi:10.1016/j.gie.2020.06.059
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Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.
BackgroundColonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias.MethodsWe did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hosp...
PMID 31981517 ... doi:10.1016/s2468-1253(19)30411-x
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Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information.
This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map.
PMID 26462083 ... doi:10.1109/tmi.2015.2487997
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Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity.
PMID 31015647 ... doi:10.1038/s41551-018-0301-3
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Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.
Background & aimsNarrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy.
PMID 29042219 ... doi:10.1053/j.gastro.2017.10.010
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Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience.
PMID 29653147 ... doi:10.1053/j.gastro.2018.04.003
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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.
ContextHistopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability.AimsWe built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide ima...
PMID 28828201 ... doi:10.4103/jpi.jpi_34_17
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Artificial intelligence and colonoscopy experience: lessons from two randomised trials.
Background and aimsArtificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known.
PMID 34187845 ... doi:10.1136/gutjnl-2021-324471
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Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis.
BackgroundArtificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality.Patients and methodsWe performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer.
PMID 32557490 ... doi:10.1055/a-1201-7165
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Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study.
BackgroundArtificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools.MethodsWe conducted Markov mode...
PMID 35430151 ... doi:10.1016/s2589-7500(22)00042-5
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Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.
Background and aimsUp to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all.
PMID 32562721 ... doi:10.1053/j.gastro.2020.06.023
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Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis.
BackgroundArtificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps.PurposeTo quantify the benefits and harms of CADe in randomized trials.DesignSystematic review and meta-analysis.
PMID 37639719 ... doi:10.7326/m22-3678
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Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).
Background & aimsArtificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously been studied in a United States (U.S.) population.MethodsWe conducted a prospective, multi-center, single-blind randomized tandem colonoscopy study to evaluate a deep-learn...
PMID 34530161 ... doi:10.1016/j.cgh.2021.09.009
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Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial.
Background and aimsArtificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.MethodsThis multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population.
PMID 35863686 ... doi:10.1016/j.cgh.2022.07.006
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Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy.
PMID 31594962 ... doi:10.1038/s41598-019-50567-5
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Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.
Background/aimTo study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy.Materials and methodsA total of 1026 patients were prospectively randomly scheduled for colonoscopy with (the CADe group, CADe) or without (the control group, CON) the aid of the CADe system, together with visual notification and voice alarm, so as to compare the detection rate of po...
PMID 31898644 ... doi:10.4103/sjg.sjg_377_19
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New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection.
PMID 31615835 ... doi:10.1136/gutjnl-2019-319914
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Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.
Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern.
PMID 30527583 ... doi:10.1016/s2468-1253(18)30282-6
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Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video).
Background and aimsArtificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice.
PMID 32745531 ... doi:10.1016/j.gie.2020.07.060
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Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.
Background and aimComputer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections.
PMID 29258081 ... doi:10.1159/000481227
Study records come from Europe PMC, which indexes PubMed, MEDLINE, PMC and preprint servers. Titles, journals, years, identifiers, citation counts and abstracts are reproduced from the source record and are not rewritten here. Listing a study is not an endorsement of its conclusion, and citation count measures attention rather than quality. This page is a starting point for your own reading, not clinical guidance.