Scoping Review Protocol: Exploring the Use of Machine Learning in the Prevention and Control of Neglected Tropical Diseases in Africa - Identifying Gaps and Challenges

Objective: The objective of this scoping review is to comprehensively examine the current state of ML applications in NTD control in Africa. Introduction: ML offers transformative potential to enhance NTD prevention and control strategies in Africa but limited studies exist for its use in Africa, where a disproportionate burden exists. There is a need to identify challenges limiting its use so as to drive innovation and progress in elimination of NTDs in Africa. Inclusion/exclusion criteria: The review will include all peer reviewed articles on the application of ML to NTDs in the African context. Will exclude articles not written in English, non-journal articles, duplicate instances, studies whose focus is not ML for NTDs, text and opinion papers, reviews that don’t represent original work, all articles for which the full text is not available/accessible and articles published after November 2023. Methods: Will search existing databases for ALL relevant peer-reviewed articles following JBI scoping review methodology and PRISMA-ScR guidelines. At least two independent reviewers will assess the articles against the inclusion criteria for the review. Potentially relevant sources will be retrieved in full and their citation details imported into JBI SUMARI. We will extract data on title, journal, year of publication, NTD, ML model(s) used, data details, data sources, data availability, objective of the study, application of the ML model(s) etc. These will be saved in an Excel database and additional data analysis performed with R statistical software, and findings will be presented through a systematic narrative synthesis in text and tables.