基于人工智能肺结核筛查技术在基层医院影像诊断中的应用价值

doi:10.19871/j.cnki.xfcrbzz.2021.02.014

新发传染病电子杂志 ›› 2021, Vol. 6 ›› Issue (2): 138-142.doi: 10.19871/j.cnki.xfcrbzz.2021.02.014

基于人工智能肺结核筛查技术在基层医院影像诊断中的应用价值

马依迪丽•尼加提, 阿里木江•阿卜杜凯尤木, 米日古丽•达毛拉, 阿布都克尤木江•阿布力孜, 田序伟, 戴国朝

喀什地区第一人民医院影像科,新疆喀什 844000

收稿日期:2021-03-12 出版日期:2021-05-31 发布日期:2021-06-24
通讯作者: 阿里木江•阿卜杜凯尤木,E-mail：3605867812@qq.com
基金资助:
1. 新疆维吾尔自治区区域协同创新专项（2020E01012）; 2. 省部共建中亚高发病成因与防治国家重点实验室开放课题（SKL-HIDCA-2020-KS3）; 3. 喀什地区第一人民医院“珠江学者•天山英才”合作专家工作室创新团队计划项目（KDYY202017）

Application value of pulmonary tuberculosis screening technology based on artificial intelligence in image diagnosis of primary hospital

Mayidili Nijiati, Alimujiang Abudukaiyoumu, Miriguli Damaola, Abudukeyoumujiang Abulizi, Tian Xuwei, Dai Guochao

Imaging Department of the First People's Hospital of Kashgar, Xinjiang Kashgar 844000, China

Received:2021-03-12 Online:2021-05-31 Published:2021-06-24

摘要/Abstract

摘要： 目的评估基于人工智能肺结核筛查技术在基层医院肺结核影像诊断中的应用价值,提高基层医院肺结核筛查水平。方法收集2019年3月至2020年7月在喀什地区第一人民医院就诊的11 616例患者的胸部X线图像及临床资料,其中10 399例作为训练集,1217例作为测试集。训练集中诊断为肺结核的病例为535例,正常病例为6972例,非肺结核异常病例为2892例。测试集中诊断为肺结核的病例为305例,正常病例为840例,非肺结核异常病例为72例。对比人工智能诊断模型与放射科医生诊断效能;对比有AI辅助的放射科医生和无AI辅助的放射科医生诊断效能。结果 ①人工智能诊断灵敏度为86.8%,高于当地初级放射医生诊断灵敏度63.3%,两种诊断方法灵敏度差异有统计学意义(P<0.05)。②在AI的辅助诊断下,当地初级放射科医生的平均诊断时间由（37.4±1.2）s降低为（14.0±4.3）s,两者差异有统计学意义（P<0.05）。结论基于人工智能肺结核筛查技术可提高肺结核影像诊断效率和效能,有利于偏远地区或基层医院进行肺结核筛查。

关键词: 人工智能, 结核病诊断, 放射医生, 胸部X线检查, 卷积神经网络

Abstract: Objective To evaluate the performance of artificial intelligence system in detecting tuberculosis (TB) in primary hospital, and improve the level of tuberculosis screening in primary hospitals. Methods The chest X-ray images and clinical data of 11616 patients in the first people's Hospital of Kashgar region from March 2019 to July 2020 were collected, among which 10 399 cases were selected as the training set and 1217 cases were selected as the test set. There were 535 TB cases, 6972 normal cases and 2892 non-tuberculosis abnormal cases in the training set, and there were 305 TB cases, 840 normal cases and 72 cases of non-tuberculosis abnormal cases in the test set. ① Comparing TB detection performances of AI system and local radiologists.② Comparing the diagnostic efficiency of radiologists with AI assistance and radiologists without AI assistance. Results ① The sensitivity of intelligent diagnosis was 86.8%, which was higher than that of local junior radiologists (63.3%). There was significant difference between the two diagnostic methods (P<0.05). ② With AI-assisted diagnosis, the average diagnosis time of local primary radiologists decreased from (37.4±1.2) s to (14.0±4.3) s, The difference was statistically significant (P<0.05). Conclusions Based on artificial intelligence tuberculosis screening technology, the efficiency and effectiveness of tuberculosis imaging diagnosis can be improved, and it is helpful to the tuberculosis screening in remote areas or grass-roots hospitals.

Key words: Artificial intelligence, Diagnosis of tuberculosis, Radiologist, Chest X-ray, Convolutional neural network

马依迪丽•尼加提, 阿里木江•阿卜杜凯尤木, 米日古丽•达毛拉, 阿布都克尤木江•阿布力孜, 田序伟, 戴国朝. 基于人工智能肺结核筛查技术在基层医院影像诊断中的应用价值[J]. 新发传染病电子杂志, 2021, 6(2): 138-142.

Mayidili Nijiati, Alimujiang Abudukaiyoumu, Miriguli Damaola, Abudukeyoumujiang Abulizi, Tian Xuwei, Dai Guochao. Application value of pulmonary tuberculosis screening technology based on artificial intelligence in image diagnosis of primary hospital[J]. Electronic Journal of Emerging Infectious Diseases, 2021, 6(2): 138-142.

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基于人工智能肺结核筛查技术在基层医院影像诊断中的应用价值

Application value of pulmonary tuberculosis screening technology based on artificial intelligence in image diagnosis of primary hospital

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