Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification.

BACKGROUND: Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine-learning methodology to characterize and validate enhancements applied to the grey-level co-occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. METHODS: One hundred patients diagnosed with age-related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high-resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland-Altman plot, while agreement between them was assessed through the Bland-Altman plot. RESULTS: Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). CONCLUSIONS: Overall, our findings suggest that a machine-learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications.

Effect of virtual reality-based exercise and physical exercise on adolescents with overweight and obesity: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity. METHODS AND ANALYSIS: This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases. TRIAL REGISTRATION NUMBER: ChiCTR2300068786.

Musculoskeletal simulation of professional ski jumpers during take-off considering aerodynamic forces.

Introduction: Musculoskeletal simulation has been widely used to analyze athletes' movements in various competitive sports, but never in ski jumping. Aerodynamic forces during ski jumping take-off have been difficult to account for in dynamic simulation. The purpose of this study was to establish an efficient approach of musculoskeletal simulation of ski jumping take-off considering aerodynamic forces and to analyze the muscle function and activity. Methods: Camera-based marker-less motion capture was implemented to measure the take-off kinematics of eight professional jumpers. A suitable full-body musculoskeletal model was constructed for the simulation. A method based on inverse dynamics iteration was developed and validated to estimate the take-off ground reaction force. The aerodynamic forces, which were calculated based on body kinematics and computational fluid dynamics simulations, were exerted on the musculoskeletal model as external forces. The activation and joint torque contributions of lower extremity muscles were calculated through static optimization. Results: The estimated take-off ground reaction forces show similar trend with the results from past studies. Although overall inconsistencies between simulated muscle activation and EMG from previous studies were observed, it is worth noting that the activation of the tibialis anterior, gluteus maximus, and long head of the biceps femoris was similar to specific EMG results. Among lower extremity extensors, soleus, vastus lateralis, biceps femoris long head, gluteus maximus, and semimembranosus showed high levels of activation and joint extension torque contribution. Discussion: Results of this study advanced the understanding of muscle action during ski jumping take-off. The simulation approach we developed may help guide the physical training of jumpers for improved take-off performance and can also be extended to other phases of ski jumping.