(1) Telerehabilitation (TR) is a part of telemedicine involved in providing rehabilitation services to people in remote locations. TR in physical therapy in the kingdom of Saudi Arabia is still in its infancy and its implementation may pose different challenges in the physical therapy settings. The purpose of this nation-wide survey is to explore physiotherapists (PTs) knowledge, attitudes, and barriers towards implementation of TR in physical therapy settings; (2) Methods: A 14 item questionnaire was developed and mailed to PTs working in hospitals and rehabilitation centers across 13 provinces in Saudi Arabia; (3) Results: 347 PTs responded. Results are as follows: 58.8% (n = 204) of PTs reported that they had sufficient knowledge about TR. About31.7% (n = 110) of PTs reported that their hospital and rehabilitation center had installed TR, yet only 19.9% (n = 69) utilized the TR facility. Image-based TR was more frequently used (n = 33) as compared to sensor-based TR (n = 29) and virtual reality TR (n = 10).The main barriers were technical issues and cost related to implement TR in physical therapy settings; and (4) Conclusions: There is a relatively high number of PTs with self-reported knowledge about TR, however facilities and usage were limited. The main barriers were technical issues, staff skills, and the high cost involved in the introduction of TR in the PT-based health care settings.

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Objective: Infantile hemiplegia due to brain injury is associated with poor attention span, which critically affects the learning and acquisition of new skills, especially among children with left-sided infantile hemiplegia (LSIH). This study aimed to improve the selective visual attention (SVA) of children with LSIH through transcranial direct current stimulation (tDCS). Methods: A total of 15 children participated in this randomized, double-blinded, pilot study; of them, 10 experienced LSIH, and the remaining 5 were healthy age-matched controls. All the children performed the Computerized Stroop Color-Word Test (CSCWT) at baseline, during the 5th and 10th treatment sessions, and at follow-up. The experimental (n = 5) and control groups (n = 5) received tDCS, while the sham group (n = 5) received placebo tDCS. All three groups received cognitive training on alternate days, for 3 weeks, with the aim to improve SVA. Results: Two-way repeated measures analysis of variance (ANOVA) showed a statistically significant change in the mean scores of CSCWT between time points (baseline, 5th and 10th sessions, and follow-up) within-subject factor, group (experimental, sham) between-subject factor and interaction (time points X group) (p < 0.005). Furthermore, a one-way repeated measures ANOVA showed significant differences between time point (p < 0.005) for the experimental and control group but not the sham group. Conclusion: These pilot results suggest that future research should be conducted with adequate samples to enable conclusions to be drawn.

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