Towards incorporating data-driven artificial intelligence-based tools in tuberculosis diagnosis in resource-constrained countries:A scoping reviewOA

Background:Tuberculosis(TB)continues to disproportionately decimate people in resource-constrained coun-tries,despite the disease being curable and preventable.Despite several TB containment measures,such as screening and diagnosis,thousands of mortalities are still recorded daily,worldwide,making it a serious global public health concern.The inherent challenges faced by healthcare systems in resource-constrained countries make their residents susceptible to TB-related deaths,especially when coupled with the double burden of disease arising from human immunodeficiency virus(HIV)co-infection.While researchers are increasingly interested in applying artificial intelligence(AI)to TB detection,there is a dearth of reviews that organise such literature.Methods:Preferred Reporting Items for Systematic and Meta-Analyses extension for Scoping Reviews(PRISMAScR)methodology guided the reporting of this scoping review on deep and machine learning models deployed to tackle TB in resource-limited settings.Findings:Machine learning algorithms,like logistic regression,k-nearest neighbour,and support vector machine,and deep learning algorithms like long short-term memory and convolutional neural networks,have been used to detect TB and performed well based on the performance evaluation metrics.However,various challenges still exist in resource-constrained countries,such as a shortage of localised,large-scale,real-time,and well-annotated datasets,high chest x-ray hardware expenses,limited integration with local health information systems,limited application in different subgroups,limited capability in detecting non-TB abnormalities,and limited sputumbased detection.Interpretation:Data-driven AI-based tools present unmatched opportunities to end TB in resource-constrained contexts through enhanced,accurate,and timely TB detection.However,concerted efforts are needed to over-come the identified challenges by investing in localised datasets and integrating AI with existing healthcare infrastructure.