Lung cysts are commonly seen on computed tomography (CT), and cystic lung diseases show a wide disease spectrum. Thus, correct diagnosis of cystic lung diseases is a challenge for radiologists. As the first diagnostic step, cysts should be distinguished from cavities, bullae, pneumatocele, emphysema, honeycombing, and cystic bronchiectasis. Second, cysts can be categorized as single/localized versus multiple/diffuse. Solitary/localized cysts include incidental cysts and congenital cystic diseases. Multiple/diffuse cysts can be further categorized according to the presence or absence of associated radiologic findings. Multiple/diffuse cysts without associated findings include lymphangioleiomyomatosis and Birt-Hogg-Dubé syndrome. Multiple/diffuse cysts may be associated with ground-glass opacity or small nodules. Multiple/diffuse cysts with nodules include Langerhans cell histiocytosis, cystic metastasis, and amyloidosis. Multiple/diffuse cysts with ground-glass opacity include pneumocystis pneumonia, desquamative interstitial pneumonia, and lymphocytic interstitial pneumonia. This stepwise radiologic diagnostic approach can be helpful in reaching a correct diagnosis for various cystic lung diseases.

We investigated the association between seasonal influenza vaccination in South Korea and socioeconomic status (SES) as well as other potential related factors. The study was based on data obtained in the Korea National Health and Nutrition Examination Survey from 2010 to 2011. Education level and household income were used as indicators for SES. Univariate and multiple logistic regression analyses were used to evaluate SES and other demographic variables as related factors for influenza vaccination, the primary outcome. Higher household income was positively associated with higher vaccine uptake in the younger (19–49 years) group [adjusted odds ratio (aOR) 1.55, 95% confidence interval (CI) 1.08–2.23], whereas the low-income and low-education group had increased vaccination coverage than the middle-income and middle-education group in the older (≥ 50 years) group (aOR 1.36, 95% CI 1.09–1.69). Current smokers tend to be unvaccinated in all age groups. Among individuals aged ≥ 50, older age, mild to moderate alcohol consumption, regular exercise, and having co-morbidities were positively associated with vaccination, while those who self-reported their health status as good were less likely to be vaccinated. The relationship between SES and seasonal influenza vaccination coverage differed between the age groups throughout the adult South Korean population. Public health policies need to address these inequalities.

Lee KC, et al. Korean J Radiol. 2021 Oct;22:e127. https://doi.org/10.3348/kjr.2020.1468

This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes.

This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887–0.918), 0.925 (95% C.I. 0.911–0.939), and 0.808 (95% C.I. 0.748–0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66–0.83) to 0.85 (95% C.I. 0.77–0.93) and from 0.71 (95% C.I. 0.62–0.80) to 0.80 (95% C.I. 0.71–0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed.