METHODS: In-depth individual interviews with thematic saturation were conducted between May and July 2018. The data was analyzed using thematic analysis.

RESULTS: Based on expert opinion, diagnosis of severe dengue is challenging as it depends on astute clinical interpretation of non-dengue-specific clinical and laboratory findings. A specific test that detects impending manifestation of severe dengue could 1) overcome failure in identifying severe disease for referral or admission, 2) facilitate timely and appropriate management of plasma leakage and bleeding, 3) overcome the lack of clinical expertise and laboratory diagnosis in rural health settings. The most important feature of any diagnostics for severe dengue is the point-of-care (POC) format where it can be performed at or near the bedside.

Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.

Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.