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ML technologies for diagnosing and treatment of tuberculosis: a survey

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Abstract

Purpose

The goal of this review is to provide a comprehensive overview of ML technologies used to diagnose, detect, predict, monitor, treat, control, and manage TB. In addition, the study aimed to present future challenges, research directions, and recommendations for diagnosing, detecting, predicting, and monitoring TB treatment using ML technologies.

Methods

Review of published papers regarding diagnosis, detection, prediction, and monitoring of TB treatment, using ML technologies. In line with other TB case studies and reports of organizational institutionalism and implementation studies for a digital health.

Results

The reviewed related research has successfully demonstrated that the application of ML technologies in the diagnosis, detection, prediction, monitoring, treatment, control and management of TB plays an important role in improving the quality of TB care and human health. The literature analyzed identified the key areas including future challenges, research directions and recommendations for diagnosing, detecting, predicting and monitoring TB treatment using ML technologies.

Conclusions

Knowledge of the state-of-the-art in the application of ML technologies in TB management and the identified research directions is beneficial for researchers and healthcare experts. It is recommended that policymakers should develop a mechanism to support the adoption of best practice of ML technologies regulations in the country's healthcare sector.

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Acknowledgements

We thank the anonymous reviewers for their insightful comments and suggestions that helped us improve the quality of the paper. This work was supported in part by the government of Tanzania through the Research and Innovation Grants of Sokoine University of Agriculture (SUA).

Funding

This work was supported by the Sokoine University of Agriculture [grant number CC003].

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Authors and Affiliations

  1. AI4D Research Group, Department of Informatics and Information Technology, Sokoine University of Agriculture, P.O Box 3038, Morogoro, Tanzania

    Joan Jonathan & Alcardo Alex Barakabitze

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  1. Joan Jonathan
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  2. Alcardo Alex Barakabitze
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All authors contributed equally to this work.

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Correspondence to Joan Jonathan.

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Jonathan, J., Barakabitze, A.A. ML technologies for diagnosing and treatment of tuberculosis: a survey. Health Technol. 13, 17–33 (2023). https://doi.org/10.1007/s12553-023-00727-5

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