Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Background and Objectives: Outcomes of deferring percutaneous coronary intervention (PCI) without invasive physiologic assessment for intermediate coronary lesions is uncertain.We sought to compare long-term outcomes between medical treatment and PCI of intermediate lesions without invasive physiologic assessment. Methods: A total of 899 patients with intermediate coronary lesions between 50% and 70% diameter-stenosis were randomized to the conservative group (n=449) or the aggressive group (n=450). For intermediate lesions, PCI was performed in the aggressive group, but was deferred in the conservative group. The primary endpoint was major adverse cardiac events (MACE, a composite of all-cause death, myocardial infarction [MI], or ischemia-driven any revascularization) at 3 years. Results: The number of treated lesions per patient was 0.8±0.9 in the conservative group and 1.7±0.9 in the aggressive group (p=0.001). At 3 years, the conservative group had a significantly higher incidence of MACE than the aggressive group (13.8% vs. 9.3%; hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.00–2.21; p=0.049), mainly driven by revascularization of target intermediate lesion (6.5% vs. 1.1%; HR, 5.69; 95% CI, 2.20–14.73;p<0.001). Between 1 and 3 years after the index procedure, compared to the aggressive group, the conservative group had significantly higher incidence of cardiac death or MI (3.2% vs.0.7%; HR, 4.34; 95% CI, 1.24–15.22; p=0.022) and ischemia-driven any revascularization. Conclusions For intermediate lesions, medical therapy alone, guided only by angiography, was associated with a higher risk of MACE at 3 years compared with performing PCI, mainly due to increased revascularization.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.