Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.

Background: This study aimed to investigate the relationship between changes in physical activity patterns following a new diagnosis of type 2 diabetes mellitus (T2DM) and the risk of developing depression.Methods: This study used comprehensive diabetes data from the National Health Insurance Service of South Korea. From this dataset, we included 254,619 individuals newly diagnosed with T2DM between 2009 and 2015 who had health examination data within 2 years before and after their diagnosis date and no prior history of depression. Physical activity levels were quantified using the metabolic equivalent of task (MET) method.Results: Compared with individuals with 0 MET-min/wk of physical activity before a new T2DM diagnosis, those who increased their activity levels to 500–999 MET-min/wk after diagnosis showed a 23% reduction in the risk of depression, while an increase to ≥1,000 MET-min/wk was associated with a 25% reduction in depression risk. Conversely, individuals with 1–499 MET-min/wk before diagnosis who became inactive after diagnosis experienced a 25% increased risk of depression. A similar trend toward increased depression risk was observed among those who reduced their physical activity from 500–999 or ≥1,000 MET-min/wk.Conclusion: Changes in physical activity levels before and after a new diagnosis of T2DM significantly influence the risk of developing depression, with increased activity reducing the risk and decreased activity elevating it. This finding underscores the importance of encouraging physical activity to support mental health in patients with newly diagnosed T2DM.