Application of artificial intelligence in diagnosis of pulmonary tuberculosis

• Published in: Chinese medical journal Vol. 137; no. 5; pp. 559 - 561
• Main Authors: Du, Jingli, Su, Yue, Qiao, Juan, Gao, Shang, Dong, Enjun, Wang, Ruilan, Nie, Yanhui, Ji, Jing, Wang, Zhendong, Liang, Jianqin, Gong, Wenping
• Format: Journal Article
• Language: English
• Published: China Lippincott Williams & Wilkins Ovid Technologies 05-03-2024; Lippincott Williams & Wilkins; Wolters Kluwer
• Subjects: Artificial Intelligence; Clinical outcomes; Data mining; Diagnosis, Computer-Assisted; Feature extraction; Humans; Machine learning; Medical diagnosis; Medical Progress; Medical technology; Pathology; Radiology; Software; Tuberculosis; Tuberculosis, Pulmonary - diagnostic imaging
• ISSN: 0366-6999; 2542-5641
• DOI: 10.1097/CM9.0000000000003018

Developing an AI-based automated screening platform in remote and resource-poor regions can significantly reduce the waiting time for patients to receive diagnostic reports, improve the efficiency of hospital outpatient clinics, and increase the accuracy of disease diagnosis. In summary, AI-assisted CXR diagnosis has potential advantages in the rapid localization and quantification of PTB lesions, which can not only accurately identify microscopic lesions often missed by the human eye, but also assist in the diagnosis and improve the detection rate of PTB. A previous study investigated the prediction model of silicosis and TB based on machine learning algorithms, statistical analysis, and data mining, and showed that the accuracy of diagnosis was 83.1%,[9] indicating the important role of AI in differential diagnosis. Pneumonia is a common lung disease, and the accuracy of sputum smear to differentiate pediatric pneumonia and PTB is less than 50% due to the low bacterial content. [...]the diagnosis mainly depends on radiological examination.