TY - JOUR
T1 - Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies
T2 - The STARD-AI protocol
AU - Sounderajah, Viknesh
AU - Ashrafian, Hutan
AU - Golub, Robert M.
AU - Shetty, Shravya
AU - De Fauw, Jeffrey
AU - Hooft, Lotty
AU - Moons, Karel
AU - Collins, Gary
AU - Moher, David
AU - Bossuyt, Patrick M.
AU - Darzi, Ara
AU - Karthikesalingam, Alan
AU - Denniston, Alastair K.
AU - Mateen, Bilal Akhter
AU - Ting, Daniel
AU - Treanor, Darren
AU - King, Dominic
AU - Greaves, Felix
AU - Godwin, Jonathan
AU - Pearson-Stuttard, Jonathan
AU - Harling, Leanne
AU - McInnes, Matthew
AU - Rifai, Nader
AU - Tomasev, Nenad
AU - Normahani, Pasha
AU - Whiting, Penny
AU - Aggarwal, Ravi
AU - Vollmer, Sebastian
AU - Markar, Sheraz R.
AU - Panch, Trishan
AU - Liu, Xiaoxuan
N1 - Funding Information:
Funding Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC). GC is supported by the NIHR Oxford Biomedical Research Centre and Cancer Research UK (programme grant: C49297/A27294). DT is funded by National Pathology Imaging Co-operative, NPIC (Project no. 104687) is supported by a £50m investment from the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). FG is supported by the National Institute for Health Research Applied Research Collaboration Northwest London.
Publisher Copyright:
©
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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.
AB - 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.
KW - health informatics
KW - protocols & guidelines
KW - quality in health care
UR - http://www.scopus.com/inward/record.url?scp=85108966060&partnerID=8YFLogxK
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U2 - 10.1136/bmjopen-2020-047709
DO - 10.1136/bmjopen-2020-047709
M3 - Article
C2 - 34183345
AN - SCOPUS:85108966060
SN - 2044-6055
VL - 11
JO - BMJ Open
JF - BMJ Open
IS - 6
M1 - e047709
ER -