结核病是严重危害人类健康的重大传染病之一，自 2014 年开始已超过人类免疫缺陷病毒感染/获得性免疫缺陷综合征位居由单一病原引起患者死亡的传染病之首。我国是全球第三大结核病高负担国家，2016 年新发结核病例约为 90 万人。我国面临着严峻的结核疫情，尤其对于结核病的早期诊断和疑难结核病误诊漏诊更导致治疗的延迟和结核病的传播。随着人工智能在医学领域的应用，机器学习和深度学习方法在结核病的诊断中体现了重要价值。该文阐述了机器学习和深度学习在结核病诊断中的应用现状和未来的发展方向。
Tuberculosis is one of the major infectious diseases that seriously endanger human health. Since 2014, it has surpassed human immunodeficiency virus/acquired immunodeficiency syndrome as the first infectious disease in patients with single pathogens. China is the third-largest country in the world in terms of high burden of tuberculosis. In 2016, there were about 900 000 new cases of tuberculosis in China. China is facing a severe tuberculosis epidemic, especially for the early diagnosis of tuberculosis and misdiagnosis of tuberculosis, which leads to delay in treatment and the spread of tuberculosis. With the application of artificial intelligence in the medical field, machine learning and deep learning methods have shown important value in the diagnosis of tuberculosis. This article will explain the application status and future development of machine learning and deep learning in the diagnosis of tuberculosis.
引用本文： 焦琳, 胡雪姣, 应斌武. 基于人工智能策略优化结核病诊断. 华西医学, 2018, 33(8): 935-938. doi: 10.7507/1002-0179.201807067 复制