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Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial...

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Autores principales: Sounderajah, Viknesh, Ashrafian, Hutan, Golub, Robert M, Shetty, Shravya, De Fauw, Jeffrey, Hooft, Lotty, Moons, Karel, Collins, Gary, Moher, David, Bossuyt, Patrick M, Darzi, Ara, Karthikesalingam, Alan, Denniston, Alastair K, Mateen, Bilal Akhter, Ting, Daniel, Treanor, Darren, King, Dominic, Greaves, Felix, Godwin, Jonathan, Pearson-Stuttard, Jonathan, Harling, Leanne, McInnes, Matthew, Rifai, Nader, Tomasev, Nenad, Normahani, Pasha, Whiting, Penny, Aggarwal, Ravi, Vollmer, Sebastian, Markar, Sheraz R, Panch, Trishan, Liu, Xiaoxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240576/
https://www.ncbi.nlm.nih.gov/pubmed/34183345
http://dx.doi.org/10.1136/bmjopen-2020-047709
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author Sounderajah, Viknesh
Ashrafian, Hutan
Golub, Robert M
Shetty, Shravya
De Fauw, Jeffrey
Hooft, Lotty
Moons, Karel
Collins, Gary
Moher, David
Bossuyt, Patrick M
Darzi, Ara
Karthikesalingam, Alan
Denniston, Alastair K
Mateen, Bilal Akhter
Ting, Daniel
Treanor, Darren
King, Dominic
Greaves, Felix
Godwin, Jonathan
Pearson-Stuttard, Jonathan
Harling, Leanne
McInnes, Matthew
Rifai, Nader
Tomasev, Nenad
Normahani, Pasha
Whiting, Penny
Aggarwal, Ravi
Vollmer, Sebastian
Markar, Sheraz R
Panch, Trishan
Liu, Xiaoxuan
author_facet Sounderajah, Viknesh
Ashrafian, Hutan
Golub, Robert M
Shetty, Shravya
De Fauw, Jeffrey
Hooft, Lotty
Moons, Karel
Collins, Gary
Moher, David
Bossuyt, Patrick M
Darzi, Ara
Karthikesalingam, Alan
Denniston, Alastair K
Mateen, Bilal Akhter
Ting, Daniel
Treanor, Darren
King, Dominic
Greaves, Felix
Godwin, Jonathan
Pearson-Stuttard, Jonathan
Harling, Leanne
McInnes, Matthew
Rifai, Nader
Tomasev, Nenad
Normahani, Pasha
Whiting, Penny
Aggarwal, Ravi
Vollmer, Sebastian
Markar, Sheraz R
Panch, Trishan
Liu, Xiaoxuan
author_sort Sounderajah, Viknesh
collection PubMed
description INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI. METHODS AND ANALYSIS: The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.
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spelling pubmed-82405762021-07-13 Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol Sounderajah, Viknesh Ashrafian, Hutan Golub, Robert M Shetty, Shravya De Fauw, Jeffrey Hooft, Lotty Moons, Karel Collins, Gary Moher, David Bossuyt, Patrick M Darzi, Ara Karthikesalingam, Alan Denniston, Alastair K Mateen, Bilal Akhter Ting, Daniel Treanor, Darren King, Dominic Greaves, Felix Godwin, Jonathan Pearson-Stuttard, Jonathan Harling, Leanne McInnes, Matthew Rifai, Nader Tomasev, Nenad Normahani, Pasha Whiting, Penny Aggarwal, Ravi Vollmer, Sebastian Markar, Sheraz R Panch, Trishan Liu, Xiaoxuan BMJ Open Health Informatics INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI. METHODS AND ANALYSIS: The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021. BMJ Publishing Group 2021-06-28 /pmc/articles/PMC8240576/ /pubmed/34183345 http://dx.doi.org/10.1136/bmjopen-2020-047709 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Health Informatics
Sounderajah, Viknesh
Ashrafian, Hutan
Golub, Robert M
Shetty, Shravya
De Fauw, Jeffrey
Hooft, Lotty
Moons, Karel
Collins, Gary
Moher, David
Bossuyt, Patrick M
Darzi, Ara
Karthikesalingam, Alan
Denniston, Alastair K
Mateen, Bilal Akhter
Ting, Daniel
Treanor, Darren
King, Dominic
Greaves, Felix
Godwin, Jonathan
Pearson-Stuttard, Jonathan
Harling, Leanne
McInnes, Matthew
Rifai, Nader
Tomasev, Nenad
Normahani, Pasha
Whiting, Penny
Aggarwal, Ravi
Vollmer, Sebastian
Markar, Sheraz R
Panch, Trishan
Liu, Xiaoxuan
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title_full Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title_fullStr Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title_full_unstemmed Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title_short Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
title_sort developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the stard-ai protocol
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240576/
https://www.ncbi.nlm.nih.gov/pubmed/34183345
http://dx.doi.org/10.1136/bmjopen-2020-047709
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