Reporting quality of studies using machine learning models for medical diagnosis: a systematic review

• Published in: BMJ open Vol. 10; no. 3; p. e034568
• Main Authors: Yusuf, Mohamed, Atal, Ignacio, Li, Jacques, Smith, Philip, Ravaud, Philippe, Fergie, Martin, Callaghan, Michael, Selfe, James
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 23.03.2020; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Accuracy; Algorithms; Bias; Biomedical research; clinical prediction; Computer Simulation; Decision making; Diagnosis, Computer-Assisted - methods; Diagnosis, Computer-Assisted - standards; Health Informatics; Humans; Machine Learning; Medical diagnosis; Medical research; Population; reporting quality; Reproducibility; Research Design - standards; Systematic review; Usability; reporting quality; medical diagnosis; clinical prediction; machine learning
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2019-034568

AimsWe conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on.MethodMedline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers.ResultsThe search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to.ConclusionAll studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings.PROSPERO registration numberCRD42018099167.