Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings

Article type: Research Article

Authors: Nijiati, Mayidili^{a; 1} | Zhang, Ziqi^{b; 1} | Abulizi, Abudoukeyoumujiang^{a; 1} | Miao, Hengyuan^b | Tuluhong, Aikebaierjiang^a | Quan, Shenwen^c | Guo, Lin^{c; *} | Xu, Tao^{b; d; e; *} | Zou, Xiaoguang^{a; *}

Affiliations: [a] The First People’s Hospital of Kashi, Xinjiang, China | [b] Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China | [c] Shenzhen Zhiying Medical Co., Ltd, Shenzhen, China | [d] Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Department of Mechanical Engineering, Tsinghua University, Beijing, China | [e] Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing, China

Correspondence: [*] Corresponding authors: Xiaoguang Zou, The First People’s Hospital of Kashi, Xinjiang, 844000, China. E-mail: [email protected]; Tao Xu, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China. E-mail: [email protected]; Lin Guo, Shenzhen Zhiying Medical Co., Ltd, Shenzhen, 518020, China. E-mail: [email protected].

Note: [1] The first three authors contributed equally to this work.

Abstract: Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of applying AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with more scant doctors and higher rates of the infected population.

Keywords: Artificial intelligence (AI), tuberculosis (TB) diagnosis, low-resource settings, radiologists, chest X-rays (CXRs), assistance, convolutional neural network

DOI: 10.3233/XST-210894

Journal: Journal of X-Ray Science and Technology, vol. 29, no. 5, pp. 785-796, 2021