Background: Screening for elbow osteochondritis dissecans (OCD) using ultrasound (US) is essential for early detection and successful conservative treatment. The aim of the study is to determine the diagnostic accuracy of YOLOv8, a deep-learning-based artificial intelligence model, for US images of OCD or normal elbow-joint images. Methods: A total of 2430 images were used. Using the YOLOv8 model, image classification and object detection were performed to recognize OCD lesions or standard views of normal elbow joints. Results: In the binary classification of normal and OCD lesions, the values from the confusion matrix were the following: Accuracy = 0.998, Recall = 0.9975, Precision = 1.000, and F-measure = 0.9987. The mean average precision (mAP) comparing the bounding box detected by the trained model with the true-label bounding box was 0.994 in the YOLOv8n model and 0.995 in the YOLOv8m model. Conclusions: The YOLOv8 model was trained for image classification and object detection of standard views of elbow joints and OCD lesions. Both tasks were able to be achieved with high accuracy and may be useful for mass screening at medical check-ups for baseball elbow.

Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries—MediaPipe and LightGBM—were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, −15°, or −30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R² and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion.

The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research.

Advanced glycation end-products (AGEs) play a critical supportive role during musculoskeletal disorders via glycosylation and oxidative stress. Though apocynin, identified as a potent and selective inhibitor of NADPH oxidase, has been reported to be involved in pathogen-induced reactive oxygen species (ROS), its role in age-related rotator cuff degeneration has not been well clarified. Therefore, this study aims to evaluate the in vitro effects of apocynin on human rotator cuff-derived cells. Twelve patients with rotator cuff tears (RCTs) participated in the study. Supraspinatus tendons from patients with RCTs were collected and cultured. After the preparation of RC-derived cells, they were divided into four groups (control group, control + apocynin group, AGEs group, AGEs + apocynin group), and gene marker expression, cell viability, and intracellular ROS production were evaluated. The gene expression of NOX, IL-6, and the receptor for AGEs (RAGE) was significantly decreased by apocynin. We also examined the effect of apocynin in vitro. The results showed that ROS induction and increasing apoptotic cells after treatment of AGEs were significantly decreased, and cell viability increased considerably. These results suggest that apocynin can effectively reduce AGE-induced oxidative stress by inhibiting NOX activation. Thus, apocynin is a potential prodrug in preventing degenerative changes of the rotor cuff.

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone’s angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation.