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Machine-learning-based artificial intelligence tools for the diagnosis of tropical fevers: a systematic review and
Shravya Chitrapady1, Rajalakshmi Rajendran1, Haritha K1
1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Introduction:
Recent advancements in diagnosing tropical fevers increasingly use artificial intelligence (AI). These innovations focus on diagnosing single or multiple diseases, significantly reducing the global burden of tropical fevers. This protocol helps to identify the key factors required for a systematic review of AI-based machine learning (ML) diagnostic test accuracy-based studies to obtain a view on the pooled performance of different types of available tools. This systematic review protocol aims to review the type of ML-based AI tools and pool the performance metrics of the currently available ML-based AI devices.
Methods And Analysis:
Patients with tropical fevers will be recruited, whereas the ML-based AI model will be the index test, and dengue, scrub typhus, leptospirosis, malaria, influenza, typhoid, chikungunya and Japanese encephalitis will be the target conditions considered for the review. Search does not restrict to any time period, and all original research studies with cross-sectional study design that are related to the development of ML tools or specific algorithms used for the diagnosis of tropical fevers from the date of inception until the date will be considered for review.Specific keywords and relevant MeSH terms for 'artificial intelligence', 'diagnosis, and 'tropical fevers' will be selected. A systematic search will be conducted in Medline/PubMed, Embase, Cochrane and Scopus covering literature from inception to February 2025. Upon retrieval of all the studies into an Excel sheet, deduplication will be done, followed by initial and secondary screening. Data extraction will be conducted using Microsoft Excel. The obtained data will be summarised narratively, and a meta-analysis of quantitative data will be performed using Meta-Disc software. The Quality Assessment of Diagnostic Accuracy Studies 2 tool will be employed to evaluate the quality of the studies. The study is planned to start in March 2025 and will be completed by September 2025.
Ethics And Dissemination:
Ethical approval is not required for this systematic review and meta-analysis, as it will use data from previously published studies. The results of the review will be published in academic journals and presented at international conferences.
Prospero Registration Number:
CRD42024516128.
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