Background and Aims: Tuberculosis (TB), a leading cause of death from infectious diseases, faced considerable challenges during the coronavirus disease 2019 (COVID‐19) pandemic. This review examines the impact of pandemic‐related disruptions, including the diversion of healthcare resources, reduced access to TB diagnostics and treatment, and declining BCG vaccination rates, on TB trends. The aim is to forecast the post‐COVID‐19 TB burden, identify risk factors that exacerbate transmission, and propose strategies to prevent a global resurgence. Methods: This narrative review incorporates epidemiological data, modeling research, and reports from the World Health Organization and national health systems. It examines TB trends before and after COVID‐19, the outcomes of coinfection, and the pandemic's impact on immunology, socioeconomic factors, and health systems. The review also compares trends in India and South Africa—two countries facing significant challenges—to those observed during the COVID‐19 pandemic. Results: COVID‐19 disruptions in healthcare led to an 18% decrease in TB notifications in 2020, resulting in delayed diagnoses, increased household transmission, and higher mortality. Immune dysregulation, including T‐cell depletion and cytokine storms, contributed to a 12.3% mortality rate in COVID‐19‐TB coinfections. Models predict a 5%–15% rise in TB incidence and an additional 1.4 million deaths by 2025. Individuals with HIV, diabetes, and malnutrition were particularly vulnerable. Factors such as overcrowding, air pollution, and reduced Bacillus Calmette–Guérin (BCG) coverage in endemic regions have further heightened susceptibility to TB. Conclusion: COVID‐19 has undone years of progress in TB control, highlighting the need for a unified health strategy. Early diagnosis, treatment of latent TB, and BCG catch‐up initiatives are crucial. Strengthening health systems, addressing socioeconomic factors such as poverty and hunger, and utilizing pandemic advancements like telemedicine and vaccine research will be key to preventing a resurgence of TB. Continued financial support and international cooperation are essential to eliminating TB by 2030.

Background and Aims: Traumatic brain injury (TBI) is a widespread global health issue with significant economic consequences. However, no existing model exists to predict the need for neurosurgical intervention in moderate TBI patients with positive initial computed tomography scans. This study determines the efficacy of machine learning (ML)‐based models in predicting the need for neurosurgical intervention.Methods: This is a retrospective study of patients admitted to the neuro‐intensive care unit of Emtiaz Hospital, Shiraz, Iran, between January 2018 and December 2020. The most clinically important variables from patients that met our inclusion and exclusion criteria were collected and used as predictors. We developed models using multilayer perceptron, random forest, support vector machines (SVM), and logistic regression. To evaluate the models, their F1‐score, sensitivity, specificity, and accuracy were assessed using a fourfold cross‐validation method.Results: Based on predictive models, SVM showed the highest performance in predicting the need for neurosurgical intervention, with an F1‐score of 0.83, an area under curve of 0.93, sensitivity of 0.82, specificity of 0.84, a positive predictive value of 0.83, and a negative predictive value of 0.83.Conclusion: The use of ML‐based models as decision‐making tools can be effective in predicting with high accuracy whether neurosurgery will be necessary after moderate TBIs. These models may ultimately be used as decision‐support tools to evaluate early intervention in TBI patients.