This study aimed to investigate the possible changes in anterior chest tightness after breast cancer surgery. We also try to investigate whether anterior chest tightness is associated with upper-limb dysfunction after breast cancer surgery. Eighty-three women who underwent breast cancer surgery were evaluated before and 2 weeks, 3 months, and 9 months after surgery. Anterior chest tightness was measured using the length of the pectoralis minor muscle through 2 methods (length from the coracoid process to the fourth rib and linear distance from the table to the posterior acromion with supine position). Shoulder range of motion and the K-DASH (Korean version of Disability Arm and Shoulder Questionnaire) score were measured to quantify functional performance of upper limb. Anterior chest tightness of patients with breast cancer significantly increased after surgery. Upper limb dysfunction was observed such as reduced shoulder range-of-motion and increased K-DASH score over time. Increase in chest tightness was correlated with shoulder range-of-motion reduction. Chest tightness was not correlated with K-DASH score directly. However, shoulder range-of-motion reduction was significantly correlated with K-DASH score. Chest tightness and upper limb dysfunction increased in breast cancer survivor. Increase in chest tightness after surgery is associated with upper limb dysfunction and careful attention is required.

Kim DY, et al. Brain Neurorehabil. 2023 Jul;16(2):e18. https://doi.org/10.12786/bn.2023.16.e18

Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual analysis by clinicians is time-consuming, complicated and prone to human error during interpretation; therefore, automated analysis using deep learning has been attempted. We aimed to develop a model for the automatic measurement of various temporal parameters of swallowing using deep learning. Overall, 547 VFSS video clips were included. Seven temporal parameters were manually measured by two physiatrists as ground-truth data: oral phase duration, pharyngeal delay time, pharyngeal response time, pharyngeal transit time, laryngeal vestibule closure reaction time, laryngeal vestibule closure duration, and upper esophageal sphincter opening duration. ResNet3D was selected as the base model for the deep learning of temporal parameters. The performances of ResNet3D variants were compared with those of the VGG and I3D models used previously. The average accuracy of the proposed ResNet3D variants was from 0.901 to 0.981. The F1 scores and average precision were 0.794 to 0.941 and 0.714 to 0.899, respectively. Compared to the VGG and I3D models, our model achieved the best results in terms of accuracy, F1 score, and average precision values. Through the clinical application of this automatic model, temporal analysis of VFSS will be easier and more accurate.

Post-stroke spasticity is a common complication that limits the functional performance of patients. Botulinum toxin (BTx) is an effective treatment for spasticity. Numerous researchers have applied extracorporeal shock wave therapy (ESWT) to address post-stroke spasticity, yielding positive clinical outcomes. We aimed to clarify the add-on effects of ESWT on BTx therapy for spasticity in patients with post-stroke. Sixteen eligible patients with upper extremity spasticity after stroke were recruited for this study. They were randomized to either a BTx with focused ESWT treatment group or a BTx alone group. Spasticity, measured using the modified Ashworth score (MAS) and modified Tardieu scale (MTS), showed statistically significant improvements in the elbow and wrist flexor muscles in both BTx + ESWT group and BTx alone groups. However, no significant differences were observed between the two groups with time flow. The BTx + ESWT group showed significantly decreased MAS of the finger flexors at follow-up and increased R1 (MTS) of the finger flexors at 3 weeks after treatment, which was not observed in the BTx alone group. This is the first study to identify the add-on effect of ESWT on BTx injections to improve post-stroke upper limb spasticity.

Park BJ, et al. Ann Rehabil Med. 2015 Feb;39(1):47-51. http://dx.doi.org/10.5535/arm.2015.39.1.47

Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS).