Purpose: This study compared the effects of the elastic resistance and general training for college Taekwondo athletes, and investigated the biomechanical ability and lower extremity function according to the movement speed of the knee joint. Methods: Twenty university student Taekwondo athletes participated voluntarily in this study. The subjects performed general resistance training for four weeks, followed by elastic resistance training for another four weeks. The biomechanical parameters during the fast and slow isokinetic conditions, Y-balance ability, and vertical jump ability were measured three times: before training, after general resistance training, and after elastic resistance training. Statistical analysis was performed under isokinetic conditions and the intervention effect. Results: The biomechanical ability differed significantly between fast and slow isokinetic conditions (p<0.05). An analysis of the training method revealed a significant difference in the maximum knee extension speed and the posterior-lateral direction of the Y-balance test (p<0.05). Conclusion Elastic resistance training had a more positive effect on the functional task performance than biomechanical factors related to muscular strength compared to general resistance training. In addition, speed-related knee biomechanical information of subjects with strong physical abilities could be utilized in sports physiotherapy.

PURPOSE: Although previous studies on the screw-home movement (SHM) for autopsy specimen and walking of living persons conducted, the possibility of acquiring SHM based on inertial measurement units received little attention. This study aimed to investigate the possibility of measuring SHM for the non-weighted bearing using a micro-electro-mechanical system-based wearable motion capture system (MEMSS). METHODS: MEMSS and camera-based motion analysis systems were used to obtain kinematic data of the knee joint. The knee joint moved from the flexion position to a fully extended position and then back to the start point. The coefficient of multiple correlation and the difference in the range of motion were used to assess the waveform similarity in the movement measured by two measurement systems. RESULTS: The waveform similarity in the sagittal plane was excellent and the in the transverse plane was good. Significant differences were found in the sagittal plane between the two systems (p<0.05). However, there was no significant difference in the transverse plane between the two systems (p>0.05). CONCLUSION The SHM during the passive motion without muscle contraction in the non-weighted bearing appeared in the entire range. We thought that the MEMSS could be easily applied to the acquisition of biomechanical data on the knee related to physical therapy.

Purpose: This study aimed to determine which interventions are effective in increasing hamstring flexibility due to changes in the range of motion (ROM) and pennation angle (PA) when foam rolling (FR) and proprioceptive neuromuscular facilitation (PNF) stretching were performed. Methods: A total of 24 healthy participants who agreed to participate in the study were randomly chosen. The participants were divided into three groups of eight people: Control, FR, and PNF stretching groups. The hip flexion angle (ROM) and PA of the hamstrings were measured before and after the experiment. The Wilcoxon signed-rank test was used to analyze the ROM and PA for each group by comparing the before and after results. The Kruskal-Wallis test and the Mann-Whitney U test were used to analyze the increase in hamstring flexibility between the groups. Results: A statistically significant difference was observed in the ROM and PA within all groups, and only the ROM was significant in the comparison between the three groups. In the comparison between the control and other two groups, a significant difference was noted in both the ROM and PA in the FR group and only the ROM in the PNF stretching group (p < 0.05). In the comparison between the FR and PNF stretching groups, no significant difference was observed (p > 0.05). Conclusion These findings FR and PNF stretching increased the ROM; however, no change in PA was observed. Therefore, FR and PNF stretching were considered effective interventions in immediately increasing hamstring flexibility.

Purpose: This study investigated the immediate biomechanical effects of unilateral mastication for 10 minutes on the temporomandibular joint (TMJ) with 21 healthy adult participants. Methods: The gum group chewed gum on the right side for 10 minutes, and the control group rested for 10 minutes. Biomechanical data were obtained using a three-dimensional infrared camera before and after intervention. An independent t-test assessed the variation of kinematic data to identify differences between before and after intervention. Results: Among biomechanical variables, the gum group’s length of the left forehead middle region and the temporomandibular joint angle decreased compared to the control group (p<0.05). Conclusion Caution with unilateral masticatory activity is recommended, as unilateral mastication causes biomechanical changes due to excessive load on the soft tissues of the contralateral TMJ.

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.