AI for Detection of Tuberculosis: Implications for Global Health.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Eui Jin Hwang, Won Gi Jeong, Pierre-Marie David, Matthew Arentz, Morten Ruhwald, Soon Ho Yoon
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引用次数: 0

Abstract

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.

人工智能检测结核病:对全球健康的影响。
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些错误,从而影响文章内容。结核病主要影响发展中国家,仍然是一个重大的全球健康问题。自 2010 年代以来,胸部放射摄影在结核病分诊和筛查中的作用不断扩大,已超出其在结核病诊断中的传统补充作用。得益于深度学习技术,用于胸片结核病检测的计算机辅助诊断(CAD)系统最近在诊断性能方面取得了重大进展。目前,肺结核计算机辅助诊断系统的性能已接近人类专家的水平,为中低收入、肺结核高发国家解决胸片解读人员不足的问题提供了一种潜在的解决方案。本文根据医学影像人工智能核对表,对现有肺结核 CAD 软件的开发过程报告进行了批判性评估。文章还探讨了扩大计算机辅助诊断解决方案的几个考虑因素,包括独立于制造商的计算机辅助诊断验证、经济和政治方面、伦理问题,以及将基于射线摄影的诊断扩展到其他非结核病的潜力。总之,结核病的计算机辅助诊断将成为具有代表性的深度学习应用,推动全球健康和健康公平的进步。©RSNA,2024。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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