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中国防痨杂志 ›› 2021, Vol. 43 ›› Issue (6): 557-561.doi: 10.3969/j.issn.1000-6621.2021.06.007

• 论著 • 上一篇    下一篇

人工智能自动阅片技术用于HIV/AIDS人群结核病主动筛查效果的研究

王前*, 何金戈, 李玉红, 陈明亭, 周林()   

  1. 四川省疾病预防控制中心结核病预防控制所(何金戈)
  • 收稿日期:2021-01-19 出版日期:2021-06-10 发布日期:2021-06-02
  • 通信作者: 周林 E-mail:zhoulin@chinacdc.cn
  • 基金资助:
    中国疾病预防控制中心科研课题(JY18-2-02)

A study on the effect of artificial intelligence automatic film reading technology in active tuberculosis screening of HIV/AIDS population

WANG Qian*, HE Jin-ge, LI Yu-hong, CHEN Ming-ting, ZHOU Lin()   

  1. *National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
  • Received:2021-01-19 Online:2021-06-10 Published:2021-06-02
  • Contact: ZHOU Lin E-mail:zhoulin@chinacdc.cn

摘要:

目的 评价结核病医学影像辅助诊断的人工智能(artificial intelligence,AI)自动阅片技术在HIV感染者和艾滋病(AIDS)患者(HIV/AIDS者)中筛查结核病的效果,为制订和完善结核病重点人群主动发现策略提供科学依据。方法 采用回顾性研究方法,选取2019年四川省凉山彝族自治州布拖县应用数字X线摄影(digital radiography, DR)进行结核病主动筛查的633例HIV/AIDS者作为研究对象,其中,47例(7.4%)为病原学阳性肺结核患者。收集研究对象胸部X线摄片(简称“胸片”)资料,同时采用3家企业的AI自动阅片技术,以及邀请3名资深专家分别进行阅片。分析不同阅片专家及AI自动阅片技术的阅片结果,并以肺结核病原学诊断结果为参照标准,比较人工阅片和AI自动阅片技术的诊断效能。结果 633例研究对象中,人工阅片诊断疑似活动性肺结核影像改变者198例(31.3%,198/633)。3名阅片专家诊断结果分别为139例(22.0%,139/633)、100例(15.8%,100/633)、90例(14.2%,90/633)。47例并发病原学阳性肺结核患者中,3名阅片专家诊断结果分别为19例(40.4%,19/47)、29例(61.7%,29/47)、21例(44.7%,21/47),共有14例(29.8%,14/47)患者均被阅片专家漏诊。AI自动化阅片技术共检出疑似活动性肺结核影像改变者434例(68.6%);3家企业AI自动化阅片技术诊断结果分别为260例(41.1%,260/633)、299例(47.2%,299/633)和247例(39.0%,247/633)。47例并发病原学阳性肺结核患者中,3家企业AI自动化阅片技术诊断结果分别为32例(68.1%,32/47)、30例(63.8%,30/47)、33例(70.2%,33/47),共有5例(10.6%)均被漏诊。以肺结核病原学诊断为参照标准,人工阅片诊断HIV/AIDS并发病原学阳性肺结核的敏感度为70.2%(33/47),特异度为71.8%(421/586),一致率为71.7%(454/633);AI自动阅片技术诊断HIV/AIDS并发病原学阳性肺结核的敏感度为89.4%(42/47),特异度为33.1%(194/586),一致率为37.3%(236/633)。结论 AI自动阅片技术在HIV/AIDS人群中诊断病原学阳性肺结核的敏感度高于人工阅片,适合用于该人群中结核病的主动筛查。

关键词: HIV, 获得性免疫缺陷综合征, 结核,肺, 人工智能, 诊断技术和方法

Abstract:

Objective To evaluate the effect of artificial intelligence (AI) automatic film reading technology in tuberculosis screening of HIV/AIDS patients, and to provide scientific basis and reference for the formulation and improvement of active tuberculosis detection strategies in high risk population. Methods A retrospective study was conducted in 633 HIV/AIDS patients who were actively screened for tuberculosis using digital X-ray photography (digital radiography, DR) in 2019. Of them, 47 (7.4%) were pathogen-positive tuberculosis patients. Data of chest X-ray (“chest film”) of the patients were read using the AI automatic film reading technology from 3 different enterprises, and 3 senior experts were also invited to read the film. The results were analyzed, and the diagnostic effectiveness of manual reading and AI automatic reading technology was compared by reference to the etiological diagnosis of pulmonary tuberculosis. Results Of the 633 subjects, 198 (31.3%, 198/633) were diagnosed as suspected active tuberculosis by manual reading. The numbers of suspected cases detected by the three reviewers were 139 (22.0%, 139/633), 100 (15.8%, 100/633), and 90 (14.2%, 90/633), respectively. Of the 47 patients complicated with pathogen-positive tuberculosis, 19 (40.4%, 19/47), 29 (61.7%, 29/47) and 21(44.7%, 21/47) were diagnosed by the 3 experts, respectively, and a total of 14 cases (29.8%, 14/47) were misdiagnosed. By AI automatic film reading technology, 434 cases (68.6%) were diagnosed as suspected active tuberculosis. Using the technologies of 3 enterprises, the numbers of suspected cases detected by were 260 (41.1%, 260/633), 299 (47.2%, 299/633) and 247 (39.0%, 247/633), respectively. Of the 47 patients complicated with pathogen-positive tuberculosis, 32 (68.1%, 32/47), 30 (63.8%, 30/47), and 33 (70.2%, 33/47) were diagnosed, respectively. A total of 5 cases (10.6%) were misdiagnosed. Based on pathogen-positive diagnosis, the sensitivity of HIV/AIDS complicated with pathogen-positive tuberculosis was 70.2% (33/47), the specificity was 71.8% (421/586), and the consistency rate was 71.7% (454/633) by manual film-reading method; while by AI automatic film reading technology, the sensitivity was 89.4% (42/47), specificity was 33.1% (194/586), and the consistency rate was 37.3% (236/633). Conclusion AI automatic film reading technology is more sensitive than manual reading methods in diagnosis of pathogen-positive tuberculosis in HIV/AIDS population, and is suitable for active screening of tuberculosis.

Key words: HIV, Acquired immunodeficiency syndrome, Tuberculosis,pulmonary, Artificial intelligence, Diagnostic techniques and procedures