The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks.

Background/Objectives: Ultrasound is the standard imaging modality to evaluate the inflammatory changes in hand joints of rheumatoid arthritis (RA) patients. However, it is operator-dependent and takes a long time to examine. In this study, we developed a radiograph-based deep learning (DL) model to support prioritization of finger joints for ultrasound (US) examination in RA patients. Methods: In this retrospective study, hand radiographs from RA patients who underwent same-day US examination of bilateral finger joints were analyzed. A DL model was developed using hand radiographs from 270 patients (2043 finger joints) to estimate joint-level likelihood of inflammatory activity. US findings served as the reference standard for model training, while clinical findings of joint tenderness and swelling were incorporated as additional tabular inputs. Model performance was evaluated in a temporal-split test cohort consisting of 40 patients (270 joints) and compared with the performance of a clinical-only logistic regression model based on joint tenderness and swelling. Results: In the test set, the DL model demonstrated higher sensitivity (82.1% vs. 38.5%), negative predictive value (96.8% vs. 90.3%), and F1-score (69.6% vs. 48.4%) than the clinical-only model. Although the area under the receiver operating characteristic curve did not differ significantly between models (p = 0.43), precision–recall (PR) analysis showed superior performance of the DL model, with a higher area under the PR curve (0.625 vs. 0.540). At the threshold maximizing the F1-score, DL-assisted triage reduced the number of finger joints selected for US examination by approximately 80%. Conclusions: A radiograph-based DL model can support efficient prioritization of finger joints for US examination in RA, offering a practical approach to enhance joint-level US triage in routine clinical practice.

Le TD, et al. J Korean Soc Radiol. 2026 Sep;87(5):786-801. https://doi.org/10.3348/jksr.2026.0082