Nationwide Study of Artificial Intelligence in Adenoma Detection for Colonoscopy
Ontology highlight
ABSTRACT:
DISEASE(S): Colonic Adenoma,Colonic Polyps,Colorectal Cancer,Colonic Polyp,Colo-rectal Cancer,Adenoma
PROVIDER: 60929 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:The goal of this cluster randomized multicenter controlled clinical trial (RCT) is to investigate whether a combined real time computer-aided polyp detection (CADe) and computer-aided polyp characterization (CADx) system (GI Genius, Medtronic) can increase the adenoma detection rate (ADR) and reduce the performance variability among endoscopists.
Participants will be randomized (1:1) to either receive an AI-assisted colonoscopy (AIC) or a conventional colonoscopy (CC).
If there is a comparison group: Researchers will compare the AIC-group and the CC-group to see if AIC can increase the ADR significantly.
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Project description:This study is a clinical validation of PolyDeep, a computer-aided polyp detection (CADe) and characterization (CADx) system. PolyDeep Advance 3 is a multicentric randomized clinical trial comparing high definition colonoscopy with PolyDeep assisted high definition colonoscopy. The hypothesis of the study is that the PolyDeep assisted colonoscopy increases the Adenoma Detection Rate (ADR).
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Project description:Evaluation of an artificial intelligence system for polyp detection (CADe)
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Project description:The investigators hypothesize that the combined use of CADe system (ENDOAID) and mucosal exposure device (Endocuff Vision) would improve the adenoma detection rate when compared to CADe system alone.
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Project description:BackgroundArtificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of "human intelligence" to AI.ObjectiveThis study aims to comprehend radiologists' perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors.MethodsBetween December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs.ResultsIn summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI.ConclusionsThis study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.
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2024-02-15 | GSE228560 | GEO
Project description:Interventions: Real-time use of AI for polyp detection during colonoscopy
Colonoscopy without use of AI
Primary outcome(s): -Short-term primary endpoints 1. Adenoma detection rate (ADR) 2. Advanced adenoma detection rate (A-ADR) -Long-term primary endpoints 1. Incidence of colorectal cancers after 10-years follow-up 2. Mortality from colorectal cancers after 10-years follow-up
Study Design: Parallel Randomized
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Project description:Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide, with rates of CRC predicted to increase. Colonoscopy is currently the gold standard of screening for CRC. Artificial intelligence (AI) is seen as a solution to bridge this gap in adenoma detection, which is a quality indicator in colonoscopy. AI systems utilize deep neural networks to enable computer-aided detection (CADe) and computer-aided classification (CADx). CADe is concerned with the detection of polyps during colonoscopy, which in turn is postulated to help decrease the adenoma miss-rate.
In contrast, CADx deals with the interpretation of polyp appearance during colonoscopy to determine the predicted histology. Prediction of polyp histology is crucial in helping Clinicians decide on a "resect and discard" or "diagnose and leave strategy". It is also useful for the Clinician to be aware of the predicted histology of a colorectal polyp in determining the appropriate method of resection in terms of safety and efficacy. While CADe has been studied extensively in randomized controlled trials, there is a lack of prospective data validating the use of CADx in a clinical setting to predict polyp histology.
The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.
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Project description:Purpose: The purpose of this experiment is to expand the repertoire of C. elegans edited transcripts and identify the roles of ADR-1 as indirect regulator of editing and ADR-2 as the only active deaminase in vivo. Methods: Strand-specific RNA sequencing of wild-type and adr mutant worms, followed by a novel RNA variant calling and comparative analysis pipeline. Results: Despite lacking deaminase function, ADR-1 affects editing of over 60 adenosines within the 3’ UTRs of 16 different mRNAs. Furthermore, ADR-1 interacts directly with ADR-2 substrates, even in the absence of ADR-2; and mutations within its dsRNA binding domains abolished both binding and editing regulation. Conclusions: ADR-1 acts as a major regulator of editing by binding ADR-2 substrates in vivo and raises the possibility that other dsRNA binding proteins, including the inactive human ADARs, regulate RNA editing by deaminase-independent mechanisms. Strand-specific RNA sequencing of wild-type and adr mutant worms, followed by a novel RNA variant calling and comparative analysis pipeline.
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