A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis

TL;DR

This paper presents a deep learning system that automatically generates detailed CT diagnostic reports for pulmonary tuberculosis, achieving high accuracy in lesion detection, classification, and overall diagnosis, aiding clinical decision-making.

Contribution

The study introduces a novel deep learning framework combining CNNs and Bayesian methods to automate PTB CT report generation with high precision and recall.

Findings

Lesion detection recall: 85.9%

Case-level diagnosis recall: 98.7%

Lesion type classification precision: 90.9%

Abstract

We developed a deep learning model-based system to automatically generate a quantitative Computed Tomography (CT) diagnostic report for Pulmonary Tuberculosis (PTB) cases.501 CT imaging datasets from 223 patients with active PTB were collected, and another 501 cases from a healthy population served as negative samples.2884 lesions of PTB were carefully labeled and classified manually by professional radiologists.Three state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images. Transfer learning method was also utilized during this process. The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma and cavitary types simultaneously.Then the Noisy-Or Bayesian function was used to generate an overall infection probability.Finally, a quantitative diagnostic report was exported.The results showed that the recall and precision rates, from the perspective of a single lesion region of PTB, were 85.9% and 89.2% respectively. The overall recall and precision rates,from the perspective of one PTB case, were 98.7% and 93.7%, respectively. Moreover, the precision rate of the PTB lesion type classification was 90.9%.The new method might serve as an effective reference for decision making by clinical doctors.