Evidence map›Paper›PMID 34183345›Full record

ArticleBMJ open2021

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

Viknesh Sounderajah, Hutan Ashrafian, Robert M Golub, Shravya Shetty, Jeffrey De Fauw, Lotty Hooft, Karel Moons, Gary Collins, David Moher, Patrick M Bossuyt and 22 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05651360 (Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions), which is not on this map. Cited by 172 papers, 31 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
172citing papers in PubMed, 31 pooled it
12.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05651360 completednot on this mapstarted 2022, after this paper: background citation

Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions

TypeobservationalSponsorOslo University HospitalRan2022 to 2023Enrolled246ConditionsAbdominal Pain, Acute Pain, Acute Cholecystitis, Acute AppendicitisArmslow-dose CT
3 · Its place in the literature

Who cites it

172 citing papers in PubMed, 31 syntheses or guidelines pooled it, 289 citations in OpenAlex.

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  11. Guideline
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  17. Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024
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112 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

32 authors at 16 institutions in 5 countries.

Viknesh SounderajahDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.ORCID 0000-0002-4595-8402
Hutan AshrafianDepartment of Surgery and Cancer, Imperial College London, Paddington, UK hutan@imperial.ac.uk.
Robert M GolubJournal of the American Medical Association, Chicago, Illinois, USA.
Shravya ShettyGoogle Health, Pal Alto, California, USA.
Jeffrey De FauwDeepMind Technologies Ltd, London, UK.
Lotty HooftCochrane Netherlands, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
Karel MoonsCochrane Netherlands, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
Gary CollinsCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.ORCID 0000-0002-2772-2316
David MoherCentre for Journalology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.ORCID 0000-0003-2434-4206
Patrick M BossuytDepartment of Epidemiology and Data Science, Amsterdam University Medical Centres, Duivendrecht, The Netherlands.
Ara DarziDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.
Alan KarthikesalingamGoogle Health, London, UK.
Alastair K DennistonInstitute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Bilal Akhter MateenThe Alan Turing Institute, London, UK.ORCID 0000-0003-4423-6472
Daniel TingSingapore Eye Research Institute, Singapore National Eye Center, Singapore.
Darren TreanorUniversity of Leeds, Leeds, UK.
Dominic KingOptum, Paddington, London, UK.
Felix GreavesDepartment of Primary Care and Public Health, Imperial College London, London, UK.
Jonathan GodwinDeepMind Technologies Ltd, London, UK.
Jonathan Pearson-StuttardSchool of Public Health, Imperial College London, London, UK.
Leanne HarlingDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.
Matthew McInnesDepartment of Radiology, University of Ottawa, Ottawa, Ontario, Canada.
Nader RifaiHarvard Medical School, Boston, Massachusetts, USA.
Nenad TomasevDeepMind Technologies Ltd, London, UK.
Pasha NormahaniDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.
Penny WhitingSchool of Social and Community Medicine, University of Bristol, Bristol, UK.
Ravi AggarwalDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.
Sebastian VollmerThe Alan Turing Institute, London, UK.
Sheraz R MarkarDepartment of Surgery and Cancer, Imperial College London, Paddington, UK.ORCID 0000-0001-8650-2017
Trishan PanchDivision of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Xiaoxuan LiuInstitute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
STARD-AI Steering Committee
Imperial College London · GBGoogle DeepMind (United Kingdom) · GBHarvard University · USThe Alan Turing Institute · GBUniversity Hospitals Birmingham NHS Foundation Trust · GBUtrecht University · NLAmerican Medical Association · USAmsterdam University Medical Centers · NLGoogle (United Kingdom) · GBGoogle (United States) · USNuffield Orthopaedic Centre · GBOttawa Hospital · CASingapore National Eye Center · SGUniversity of Bristol · GBUniversity of Leeds · GBUniversity of Ottawa · CA

Funding

Cancer Research UK 27294Cancer Research UK C49297/A27294Medical Research Council MC_PC_18032Medical Research Council MC_PC_19005
6 · The paper itself

Abstract

introductionStandards 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.

Indexed as

Artificial IntelligenceDiagnostic Tests, RoutineHumansLondonResearch DesignResearch Reporthealth informaticsprotocols & guidelinesquality in health care

Identifiers

PMID34183345
PMCPMC8240576
OpenAlexW3173393692

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.