A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis
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A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis Xukun Li 1 & Yukun Zhou 1 & Peng Du 1 & Guanjing Lang 2 & Min Xu 2 & Wei Wu 2 Accepted: 30 October 2020 # The Author(s) 2020
Abstract The purpose of this study was to establish and validate a new deep learning system that generates quantitative computed tomography (CT) reports for the diagnosis of pulmonary tuberculosis (PTB) in clinic. 501 CT imaging datasets were collected from 223 patients with active PTB, while another 501 datasets, which served as negative samples, were collected from a healthy population. All the PTB datasets were labeled and classified manually by professional radiologists. Then, four state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images. The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma, and cavitary types. The Noisy-Or Bayesian function was used to generate an overall infection probability of this case. The results showed that the recall and precision rates of detection, from the perspective of a single lesion region of PTB, were 85.9% and 89.2%, respectively. The overall recall and precision rates of detection, from the perspective of one PTB case, were 98.7% and 93.7%, respectively. Moreover, the precision rate of type classification of the PTB lesion was 90.9%. Finally, a quantitative diagnostic report of PTB was generated including infection possibility, locations of the lesion, as well as the types. This new method might serve as an effective reference for decision making by clinical doctors. Keywords Deep learning . Computed tomography . Convolution neural network . Tuberculosis
1 Introduction Pulmonary tuberculosis (PTB) is one of the leading respiratory infectious diseases worldwide [1]. India, Indonesia, and China have the highest PTB burden [2, 3]. Also, in China, the PTB is the second-highest infectious disease after viral hepatitis [4]. Therefore, correct detection and diagnosis of PTB are crucial importance. With the rapid development of big data and artificial intelligence (AI), deep learning method [5] has been gradually applied to computer-aided diagnosis (CAD),deep learning technologies, such as the convolutional neural network (CNN) with its strong ability of nonlinear modeling, have also
* Wei Wu [email protected] 1
Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., 259 Wensan Road, Hangzhou Zhejiang 310012, People’s Republic of China
2
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou Zhejiang 310003, People’s Republic of China
been applied extensively in medical image processing. Relevant studies have been conducted on the diagnosis of pulmonary nodules, the classification of benign and malignant tumors worldwide. [6–12]. This approach has been used to improve the diagnosis of pulmonary nodules and lung canc
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