Progress and Challenges for the Application of Machine Learning for Neglected Tropical Diseases

TL;DR

This paper reviews how machine learning can improve the detection, management, and treatment of neglected tropical diseases, highlighting current applications and challenges in leveraging AI for better health outcomes in affected regions.

Contribution

It provides a comprehensive survey of machine learning applications in NTDs, identifying key challenges and future directions for research and implementation.

Findings

Machine learning enhances NTD surveillance and prediction.

AI-driven drug discovery shows promise for NTD therapeutics.

Challenges include data scarcity and model deployment in low-resource settings.

Abstract

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.