Clinical value of Artificial Intelligence in detecting and analyzing lesions in patients with pulmonary tuberculosis

Abstract

Abstract: Objective To compare the detection abilities of pulmonary nodule detection and analysis system and radiologists in the diagnosis of pulmonary tuberculosis lesions. To explore the contribution of artificial intelligence to the detection of lesions in tuberculosis patients. Methods A retrospective study was conducted to analyze the computed tomography images of tuberculosis patients from December 2016 to March 2018 in Peking University Shenzhen hospital. Firstly, CT images were retrospectively analyzed by two methods and divided into two groups, A and B. Group A was analyzed by σ-Discover/Lung pulmonary nodule detection and analysis system 1.0. Group B was analyzed by two chest radiologists. The abilities of lesions detection in tuberculosis patients in groups A and B were compared. Secondly, after reading by two experienced chest radiologists, patients with malignant pulmonary nodules or masses were excluded, and σ-Discover/Lung pulmonary nodule detection and analysis system was used to analyze the probabilities of malignant lesions (more than 50% as a high malignant criterion). Results A total of 32 patients with pulmonary tuberculosis were enrolled in the study, with a total of 413 lesions. A total of 380 lesions were detected in group A, including 10 misdetections and 43 missed examinations. A total of 407 lesions were detected in group B and with no false detections and 6 missing lesions. The detection rate of lesions in group A was 89.83%, the rate of false detection was 2.63%, and the rate of missing diagnosis was 10.17%. The detection rate of lesions in group B was 98.55%, the rate of false detection was 0%, and the rate of missing diagnosis was 1.42%. The pulmonary nodule detection and analysis system judged that the total malignant probability of 217 lesions was greater than 50%, and 57.1% of benign lung lesions were analyzed for high malignant probability. Conclusion sThe pulmonary nodule detection and analysis system has a good ability to detect the lesions of pulmonary tuberculosis patients, but the detection ability is not as good as radiologists. The rate of missing diagnosis is high, and the current accuracy of diagnosis of benign lesions of tuberculosis patients is low.

Key words: Artificial Intelligence, Tuberculosis, Detection rate, The rate of missed diagnosis, Pulmonary nodule

YAN Ming-yan, Chen Gen-ming, LAI Chao, CHEN Zhi-hong, ZHU Jun-lan, LI Jiao, DONG Shu-wen, CHENG Guan-xun. Clinical value of Artificial Intelligence in detecting and analyzing lesions in patients with pulmonary tuberculosis[J]. Electronic Journal of Emerging Infectious Diseases, 2018, 3(4): 214-217.

References

[1] World Health Organization.Global tuberculosis report 2018[EB/OL].[2018-0-18]. http://www.who.int/tb/publications/global_report/en/ .
[2] SINGH A K, GUPTA U D.Animal models of tuberculosis: Lesson learnt[J]. The Indian journal of medical research, 2018, 147(5): 456-63.
[3] FAZAL M I, PATEL M E, TYE J, et al.The past, present and future role of artificial intelligence in imaging[J]. Eur J Radiol, 2018, 105: 246-250.
[4] NISHIKAWA R M.Current status and future directions of computer-aided diagnosis in mammography[J]. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2007, 31(4-5): 224-235.
[5] KLIGERMAN S, CAI L, WHITE C S.The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph[J]. Journal of thoracic imaging, 2013, 28(4): 244-252.
[6] MASSALHA S, CLARKIN O, THORNHILL R, et al.Decision Support Tools, Systems, and Artificial Intelligence in Cardiac Imaging[J]. The Canadian journal of cardiology, 2018, 34(7): 827-838.
[7] XIONG Y, BA X, HOU A, et al.Automatic detection of mycobacterium tuberculosis using artificial intelligence[J]. Journal of thoracic disease, 2018, 10(3): 1936-1940.
[8] Y.M.LURE F, JAEGER S, ANTANI S, et al. 自动化显微镜检测和数字化胸片诊断系统在肺结核筛查中的应用[J]. 新发传染病电子杂志, 2017, 2(1): 5-9.
[9] LAKHANI P, SUNDARAM B.Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks[J]. Radiology, 2017, 284(2): 574-582.
[10] KIM D H, WIT H, THURSTON M.Artificial intelligence in the diagnosis of Parkinson's disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning[J]. Nuclear Medicine Communications, 2018, 39(10):887-893.
[11] CHUNG S W, HAN S S, LEE J W, et al.Automated detection and classification of the proximal humerus fracture by using deep learning algorithm[J]. Acta orthopaedica, 2018, 89(4): 468-473.
[12] BARBIERI C, MOLINA M, PONCE P, et al.An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients[J]. Kidney International, 2016, 90(2): 422-429.
[13] PARK W J, PARK J-B.History and application of artificial neural networks in dentistry[J]. European Journal of Dentistry, 2018, 12(4): 594-601.
[14] HOSNY A, PARMAR C, QUACKENBUSH J, et al.Artificial intelligence in radiology[J]. Nature reviews Cancer, 2018, 18(8): 500-510.
[15] PESAPANE F, CODARI M, SARDANELLI F.Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine[J]. European Radiology Experimental, 2018, 2(1): 35.