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Professor Gary S. Collins, Paula Dhiman, Constanza L. Andaur Navarro, Jie Ma, Lotty Hooft, Johannes B. Reitsma, Patrícia Logullo, Andrew L. Beam, Lily H. Peng, Ben VAN CALSTER and 3 more
INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques. METHODS AND ANALYSIS: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications. PROSPERO REGISTRATION NUMBER: CRD42019140361 and CRD42019161764.
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Collins, P. G. S., Dhiman, P., Navarro, C. L. A., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L. H., CALSTER, B. V., Smeden, M. V., Riley, R. D., & Moons, K. G. (2021). Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 11(7), e048008. https://doi.org/10.1136/bmjopen-2020-048008
Collins, Professor Gary S., et al. "Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence." BMJ Open, vol. 11, no. 7, 2021, pp. e048008, https://doi.org/10.1136/bmjopen-2020-048008.
Collins, Professor Gary S., Paula Dhiman, Constanza L. Andaur Navarro, Jie Ma, Lotty Hooft, Johannes B. Reitsma, Patrícia Logullo, et al. "Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence." BMJ Open 11, no. 7 (2021): e048008. https://doi.org/10.1136/bmjopen-2020-048008.
Collins P. G. S., Dhiman P., Navarro C. L. A. [и др.] Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence // BMJ Open. — 2021. — Vol. 11, № 7. — P. e048008. — URL: https://doi.org/10.1136/bmjopen-2020-048008
@article{collins2021protocol,
title = {{Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence}},
author = {Professor Gary S. Collins and Paula Dhiman and Constanza L. Andaur Navarro and Jie Ma and Lotty Hooft and Johannes B. Reitsma and Patrícia Logullo and Andrew L. Beam and Lily H. Peng and Ben VAN CALSTER and Maarten van Smeden and Richard D Riley and Karel G.M. Moons},
journal = {BMJ Open},
year = {2021},
volume = {11},
number = {7},
pages = {e048008},
doi = {10.1136/bmjopen-2020-048008},
url = {https://doi.org/10.1136/bmjopen-2020-048008}
}TY - JOUR AU - Collins, Professor Gary S. AU - Dhiman, Paula AU - Navarro, Constanza L. Andaur AU - Ma, Jie AU - Hooft, Lotty AU - Reitsma, Johannes B. AU - Logullo, Patrícia AU - Beam, Andrew L. AU - Peng, Lily H. AU - CALSTER, Ben VAN AU - Smeden, Maarten van AU - Riley, Richard D AU - Moons, Karel G.M. TI - Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence JO - BMJ Open VL - 11 IS - 7 SP - e048008 PY - 2021 DA - 2021/07/01 DO - 10.1136/bmjopen-2020-048008 UR - https://doi.org/10.1136/bmjopen-2020-048008 ER -
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