Advances in diagnosis and treatment of non-tuberculous mycobacterial lung disease.

The prevalence of Non-tuberculous Mycobacterial Pulmonary Disease (NTM-PD) is increasing worldwide. The advancement in molecular diagnostic technology has greatly promoted the rapid diagnosis of NTM-PD clinically, and the pathogenic strains can be identified to the species level through molecular typing, which provides a reliable basis for treatment. In addition to the well-known PCR and mNGS methods, there are numerous alternative methods to identify NTM to the species level.

Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging.

Background: To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training.

Progress on diagnosis and treatment of latent tuberculosis infection.

One fourth of the global population has been infected with , and about 5%-10% of the infected individuals with latent tuberculosis infection (LTBI) will convert to active tuberculosis (ATB). Correct diagnosis and treatment of LTBI are important in ending the tuberculosis epidemic. Current methods for diagnosing LTBI, such as tuberculin skin test (TST) and interferon-γ release assay (IGRA), have limitations.

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.

Objectives: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB).

Methods: A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model.