From data to decisions: Statistical tools and Artificial Intelligence in tuberculosis Operational Research

Q3 Medicine

Indian Journal of Tuberculosis Pub Date : 2025-09-02 DOI:10.1016/j.ijtb.2025.09.001

V.K. Arora , Nishi Aggarwal , Sanjay Rajpal

{"title":"From data to decisions: Statistical tools and Artificial Intelligence in tuberculosis Operational Research","authors":"V.K. Arora , Nishi Aggarwal , Sanjay Rajpal","doi":"10.1016/j.ijtb.2025.09.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Tuberculosis (TB) remains a major public health challenge, especially in low- and middle-income countries. Operational Research (OR), supported by robust statistical methods, plays a critical role in optimizing TB control strategies.</div></div><div><h3>Objective</h3><div>This review highlights the statistical tools applied in TB Operational Research, their applications, and the emerging role of Artificial Intelligence (AI) in strengthening data-driven decision-making.</div></div><div><h3>Methods</h3><div>We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.</div></div><div><h3>Findings</h3><div>Statistical methods underpin surveillance, diagnosis, treatment evaluation, and policy modeling in TB programs. AI-driven techniques, such as machine learning and deep learning, are expanding the analytical landscape by enhancing prediction, identifying high-risk populations, and enabling real-time program monitoring.</div></div><div><h3>Conclusion</h3><div>Statistical tools from traditional inference to AI-modeling are essential for advancing TB control. Strengthening methodological rigor, reporting standards and interdisciplinary collaboration will be pivotal in harnessing data for effective TB elimination strategies.</div></div>","PeriodicalId":39346,"journal":{"name":"Indian Journal of Tuberculosis","volume":"72 4","pages":"Pages 455-459"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Tuberculosis","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019570725001921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Tuberculosis (TB) remains a major public health challenge, especially in low- and middle-income countries. Operational Research (OR), supported by robust statistical methods, plays a critical role in optimizing TB control strategies.

Objective

This review highlights the statistical tools applied in TB Operational Research, their applications, and the emerging role of Artificial Intelligence (AI) in strengthening data-driven decision-making.

Methods

We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.

Findings

Statistical methods underpin surveillance, diagnosis, treatment evaluation, and policy modeling in TB programs. AI-driven techniques, such as machine learning and deep learning, are expanding the analytical landscape by enhancing prediction, identifying high-risk populations, and enabling real-time program monitoring.

Conclusion

Statistical tools from traditional inference to AI-modeling are essential for advancing TB control. Strengthening methodological rigor, reporting standards and interdisciplinary collaboration will be pivotal in harnessing data for effective TB elimination strategies.

查看原文本刊更多论文

从数据到决策：结核运筹学中的统计工具和人工智能。

背景：结核病（TB）仍然是一个重大的公共卫生挑战，特别是在低收入和中等收入国家。在稳健统计方法的支持下，运筹学在优化结核病控制策略方面发挥着关键作用。目的：综述了结核病运筹学中应用的统计工具及其应用，以及人工智能（AI）在加强数据驱动决策方面的新兴作用。方法：我们研究了经典的统计方法以及预测建模、成本效益分析和基于人工智能的框架。来自不同环境的案例说明了它们的实际影响。研究结果：统计方法是结核病项目监测、诊断、治疗评估和政策建模的基础。人工智能驱动的技术，如机器学习和深度学习，通过增强预测、识别高风险人群和实现实时程序监控，正在扩大分析领域。结论：从传统推断到人工智能建模的统计工具对于推进结核病控制至关重要。加强方法的严谨性、报告标准和跨学科合作对于利用数据制定有效的消除结核病战略至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Indian Journal of Tuberculosis Medicine-Infectious Diseases

CiteScore

2.80

自引率

0.00%

发文量

103

期刊介绍： Indian Journal of Tuberculosis (IJTB) is an international peer-reviewed journal devoted to the specialty of tuberculosis and lung diseases and is published quarterly. IJTB publishes research on clinical, epidemiological, public health and social aspects of tuberculosis. The journal accepts original research articles, viewpoints, review articles, success stories, interesting case series and case reports on patients suffering from pulmonary, extra-pulmonary tuberculosis as well as other respiratory diseases, Radiology Forum, Short Communications, Book Reviews, abstracts, letters to the editor, editorials on topics of current interest etc. The articles published in IJTB are a key source of information on research in tuberculosis. The journal is indexed in Medline