A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis

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.

Active/Secondary Pulmonary Tuberculosis Classification Model Based on Deep Learning of CT Images and Its Clinical Applications

“…According to previous studies, the use of deep learning models in pulmonary TB diagnosis has shown good results. Xukun L [19] used a state-of-the-art 3D convolutional neural network (CNN) model to collect 501 CT image datasets from 223 patients with active pulmonary TB and to collect 501 datasets as negative samples from healthy people. The detection recall rate and accuracy of this algorithm obtained values of 85.9% and 89.2%, respectively.…”

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

“…In the present study, a CNN based on the CenterNet detection framework managed to automatically identify suspected regions that were strongly indicative of TB with a mAP of 0.68. More recently, Li and colleagues [ 33 ] reported a state-of-the-art three-dimensional DL model to annotate the spatial location of lesions and classify five critical CT imaging types of TB disease (miliary, infiltrative, caseous, tuberculoma, and cavitary), with a classification precision rate at 90.9%. The overall accuracy of the proposed model was similar (0.86–0.92 vs. 0.91) for six typical CT imaging findings.…”

A Fully Automatic AI-based CT Image Analysis System for Accurate Detection, Diagnosis, and Quantitative Severity Evaluation of Pulmonary Tuberculosis

“…The DL system managed to automatically discover the suspected regions that are strongly indicative of TB with a mAP of 0.68. More recently, Li and colleagues [31] reported a state-of-the-art 3D deep learning model to annotate the spatial location of lesions and classify ve critical CT imaging types of TB disease (miliary, in ltrative, caseous, tuberculoma, and cavitary), with classi cation precision rate at 90.9%. Our model has shown similar overall accuracy (0.86-0.92 vs. 0.91) for six typical CT imaging ndings.…”