Ahmad, OF, Mori, Y, Misawa, M et al. (20 more authors) (2020) Establishing key research questions for the implementation of artificial intelligence in colonoscopy - a modified Delphi method. Endoscopy. ISSN 0013-726X
Abstract
Background and Aims
Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities.
Methods
An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions.
Results
The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient.
Conclusions
This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society wm150122 EPSRC (Engineering and Physical Sciences Research Council) EP/P027938/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Nov 2020 14:07 |
Last Modified: | 26 Nov 2020 14:07 |
Status: | Published online |
Publisher: | Thieme Gruppe |
Identification Number: | 10.1055/a-1306-7590 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168363 |