Fei Zhang ¹ Maomao Li ² Ming-Lin Li ¹ Tian Tian ¹ Guilei Zhang ^1* Yang Zhenrong ^3* Feng Guo ^4* Hui Han ^5* Yuting Wang ^6* Jia-He Wang ^1* Ying Liu ^7*

• ¹ Department of Family Medicine,Shengjing Hospital of China Medical University, Shenyang, China
• ² Department of General Practice, First Affiliated Hospital of Anhui Medical University, Hefei, China
• ³ Department of Pulmonary and Critical Care Medicine, Anshan Central Hospital, Anshan, China
• ⁴ Department of Emergency Medicine, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
• ⁵ Science and Technology Research Center of China Customs, Beijing, China
• ⁶ Department of heart function, ShengJing Hospital of China Medical University, Shenyang, China
• ⁷ Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China

The mortality rate associated with mycobacterium tuberculosis (MTB) has seen a significant rise in regions heavily affected by the disease over the past few decades. The traditional methods for diagnosing and differentiating tuberculosis (TB) remain thorny issues, particularly in areas with a high TB epidemic and inadequate resources. Processing numerous images can be time-consuming and tedious. Therefore, there is a need for automatic segmentation and classification technologies based on lung computed tomography (CT) scans to expedite and enhance the diagnosis of TB, enabling the rapid and secure identification of the condition. Deep Learning (DL) offers a promising solution for automatically segmenting and classifying lung CT scans, expediting and enhancing TB diagnosis. This review evaluates the diagnostic accuracy of DL modalities for diagnosing pulmonary tuberculosis (PTB) after searching the PubMed and Web of Science databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Seven articles were found and included in the review. While DL has been widely used and achieved great success in CT-based PTB diagnosis, there are still challenges to be addressed and opportunities to be explored, including data scarcity, model generalization, interpretability, and ethical concerns. Addressing these challenges requires data augmentation, interpretable models, moral frameworks, and clinical validation. Further research should focus on developing robust and generalizable DL models, enhancing model interpretability, establishing ethical guidelines, and conducting clinical validation studies. DL holds great promise for transforming PTB diagnosis and improving patient outcomes.