Reporting quality of studies using machine learning models for medical diagnosis: a systematic review. Issue 3 (23rd March 2020)

• Record Type: Journal Article
• Authors: Yusuf, Mohamed; Atal, Ignacio; Li, Jacques; Smith, Philip; Ravaud, Philippe; Fergie, Martin; Callaghan, Michael; Selfe, James
• Is Part Of: BMJ open. Volume 10:Issue 3(2020)
• Publication Date: 2020-03-23
• Subjects: machine learning -- medical diagnosis -- clinical prediction -- reporting quality; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-034568
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Aims: We 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. Method: Medline 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. Results: The 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. Conclusion: All 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 number: CRD42018099167 .