The fracture of the elbow is common in human beings. The complex structure of the elbow, including its irregular shape, border, etc., makes it difficult to correctly recognize elbow fractures. To address such challenges, a method is proposed in this work that consists of two phases. In Phase I, pre-processing is performed, in which images are converted into RGB. In Phase II, pre-trained convolutional models Darknet-53 and Xception are used for deep feature extraction. The handcrafted features, such as the histogram of oriented gradient (HOG) and local binary pattern (LBP), are also extracted from the input images. A principal component analysis (PCA) is used for best feature selection and is serially merged into a single-feature vector having the length of N×2125. Furthermore, informative features N×1049 are selected out of N×2125 features using the whale optimization approach (WOA) and supplied to SVM, KNN, and wide neural network (WNN) classifiers. The proposed method's performance is evaluated on 16,984 elbow X-ray radiographs that are taken from the publicly available musculoskeletal radiology (MURA) dataset. The proposed technique provides 97.1% accuracy and a kappa score of 0.943% for the classification of elbow fractures. The obtained results are compared to the most recently published approaches on the same benchmark datasets.
Purpose: This study surveyed physiotherapists working at Canadian CF specialized centres to investigate the current practice, barriers to, and facilitators of exercise testing and training. Method: Physiotherapists were recruited from 42 Canadian CF centres. They responded to an e-questionnaire regarding their practice. The data were analyzed using descriptive statistics. Results: Eighteen physiotherapists responded (estimated response rate of 23%); median years of clinical experience was 15 (range, min-max,3-30) years. Aerobic testing was administered by 44% of respondents, strength testing by 39%, aerobic training by 78%, and strength training by 67%. The most frequently reported barriers across all four types of exercise testing and training were insufficient funding (reported by 56-67% of respondents), time (50-61%) and staff availability (56%). More late career than early career physiotherapists reported utilizing aerobic testing by (50% vs. 33% of respondents), strength testing (75% vs 33%), aerobic training (100% vs. 67%), and strength training (100% vs. 33%). Conclusions: Exercise testing and training is underutilized in Canadian CF centres. Experienced PTs reported utilizing exercise testing and training more than less-experienced physiotherapists. Post-graduate education and mentorship, especially for less-experienced clinicians, are recommended to emphasize the importance of exercise testing and training. Barriers of funding, time, and staff availability should be addressed to further improve quality of care.
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