Global Research Trends in Machine Learning and Scoring Systems for Drug-Resistant Tuberculosis Outcome Prediction: A Bibliometric Analysis

Abstract

Drug-Resistant Tuberculosis (DR-TB) continues to be a serious worldwide health concern with a relatively low cure rate, highlighting the importance of timely detection of individuals likely to experience unfavorable treatment results which remains crucial. This study aims to map the global research landscape on the use of machine learning (ML) models and traditional scoring systems (SS) in predicting DR-TB treatment outcomes, focusing on research trends, intellectual structures, and collaboration networks. A systematic and quantitative bibliometric analysis was conducted on 37 eligible studies retrieved from the Scopus and PubMed databases, covering publications from 2015 to 2025. Visualization of publication trends, keyword co-occurrence, and collaboration patterns among authors, institutions, and countries was performed using VOSviewer (version 1.6.20). The findings show that publication output was limited prior to 2021 but increased substantially from 2022 onward. Scoring system-based studies accounted for the largest proportion (57%), followed by ML-based approaches (40%), while hybrid ML-SS models were relatively rare (3%). Highly cited studies were predominantly produced by research groups based in the United Kingdom, United States, and China, frequently focusing on radiomics, deep learning, and drug exposure-response modeling. Keyword and temporal overlay analyses indicate a shift from conventional risk-factor and scoring-based epidemiological models toward data-driven predictive approaches. Collaboration networks reveal analysis further demonstrates strong intra-regional partnerships but relatively limited cross-cluster integration. These findings suggest that although machine learning model development is concentrated in high-resource settings, scoring models remain essential for practical implementation in high-burden, resource-limited regions, and the limited number of hybrid approaches highlights the need for integrative models that balance predictive performance with feasibility.

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