Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. Despite the improvements in diagnostic techniques, the accuracy of TB diagnosis is still low. In recent years, the development of artificial intelligence (AI) has opened up new possibilities in diagnosing and treating TB with high accuracy compared to traditional methods. Traditional diagnostic techniques, such as sputum smear microscopy, culture tests, and chest X-rays, are time-consuming, with less sensitivity for the detection of TB in patients. Due to the new developments in AI, advanced diagnostic and treatment techniques have been developed with high accessibility, speed, and accuracy. AI, including various specific methodologies, is becoming vital in managing TB. Machine learning (ML) methodologies, such as support vector machines (SVMs) and random forests (RF), alongside deep learning (DL) technologies, particularly convolutional neural networks (CNNs) for image analysis, are employed to analyze diverse patient data, including medical images and biomarkers, to enhance the accuracy and speed of tuberculosis diagnosis. This study summarized the benefits and drawbacks of both traditional and AI-driven TB diagnosis, highlighting how AI can support traditional techniques to increase early detection, lower misdiagnosis, and strengthen international TB control initiatives.

Tuberculosis (TB) and tumor, with similarities in immune response and pathogenesis, are diseases that are prone to produce autoimmune stress response to the host immune system. With a symbiotic relationship between the two, TB can facilitate the occurrence and development of tumors, while tumor causes TB reactivation. In this review, we systematically sorted out the incidence trends and influencing factors of TB and tumor, focusing on the potential pathogenesis of TB and tumor, to provide a pathway for the co-pathogenesis of TB comorbid with tumor (TCWT). Based on this, we summarized the latest progress in the diagnosis and treatment of TCWT, and provided ideas for further exploration of clinical trials and new drug development of TCWT.

Background: Despite extensive knowledge of tuberculosis (TB) and its control, there remains a significant gap in understanding the comprehensive impact of the COVID-19 pandemic on TB incidence patterns. This study aims to explore the impact of COVID-19 on the pattern of pulmonary tuberculosis in China and examine the application of time series models in the analysis of these patterns, providing valuable insights for TB prevention and control. Methods: We used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007–2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020–2023), using deviations between predicted and actual values to reflect the impact of COVID-19 countermeasures on PTB incidence. Results: Prior to the COVID-19 outbreak, PTB incidence in China exhibited a steady decline with strong seasonal fluctuations, characterized by two annual peaks—one in March and another in December. These seasonal trends persisted until 2019. During the COVID-19 pandemic, there was a significant reduction in PTB cases, with actual reported cases falling below the predicted values. The disruption in PTB incidence appears to be temporary, as 2023 data indicate a gradual return to pre-pandemic trends, though the incidence rate remains slightly lower than pre-COVID levels. Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. We found that the Prophet model had the lowest values for all three indexes, demonstrating the best fitting and prediction performance. Conclusions: The COVID-19 pandemic has had a temporary but significant impact on PTB incidence in China, leading to a reduction in reported cases during the pandemic. However, as pandemic control measures relax and the healthcare system stabilizes, PTB incidence patterns are expected to return to pre-COVID-19 levels. The Prophet model demonstrated the best predictive performance and proves to be a valuable tool for analyzing PTB trends and guiding public health planning in the post-pandemic era.

Tuberculosis (TB) remains a major public health challenge in China despite substantial long-term progress. Using data from the Global Burden of Disease Study 2023, this study reassessed trends and determinants of TB burden in China from 1990 to 2023. Age-standardized incidence, mortality, and disability-adjusted life year (DALY) rates were analyzed using estimated annual percentage change, age–period–cohort modeling, and demographic decomposition, with comparative risk assessment to quantify behavioral and metabolic contributions. Between 1990 and 2023, age-standardized incidence, mortality, and DALY rates declined by approximately 73.24%, 94.00%, and 92.40%, respectively. Negative net and local drift values indicated sustained reductions across age groups; however, the decline slowed after 2021, with a modest rebound in incidence. Since 2015, reductions in incidence have been more moderate than the pace required to achieve the 2035 End TB Strategy targets. Decomposition analysis demonstrated that improvements in age-specific rates were the primary drivers of long-term reductions, whereas demographic shifts—particularly population aging—partially offset these gains. The burden increasingly shifted toward older adults, and males consistently experienced higher rates than females. Tobacco and alcohol use contributed substantially to sex differentials, while undernutrition and metabolic disorders remained relevant risk factors. These findings indicate that China’s TB epidemic has entered a phase shaped by demographic aging and evolving risk structures, requiring sustained and adaptive control efforts.