Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset.Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist’s diagnosis record.Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset.Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

Pressure-induced skin blood flow responses measured via laser Doppler flowmetry are commonly reported in the time domain. The usefulness of spectral analysis in examining blood flow control mechanisms has been demonstrated, but traditional Fourier analysis does not provide sufficient resolution to reveal characteristic low frequencies.

Background and Methods: This paper reviews the current capacity of personnel in enabling access to assistive technology (AT) as well as the systems and processes within which they work, and was reviewed, discussed, and refined during and following the Global Research, Innovation, and Education in Assistive Technology (GREAT) Summit. Findings: Key concepts addressed include a person-centred team approach; sustainability indicators to monitor, measure, and respond to needs for service design and delivery; education, research, and training for competent practice, using the six rehab-workforce challenges framework; and credentialing frameworks. We propose development of a competence framework and associated education and training programs, and development and implementation of a certification framework for AT personnel. Conclusions: There is a resolve to address the challenges faced by People globally to access assistive technology. Context specific needs assessment is required to understand the AT Personnel landscape, to shape and strengthen credentialing frameworks through competencies and certification, acknowledging both general and specific skill mix requirements.

Diabetic foot ulcers are one of the most serious complications associated with diabetes mellitus. Current research studies have demonstrated that biomechanical alterations of the diabetic foot contribute to the development of foot ulcers. However, the changes of soft tissue biomechanical properties associated with diabetes and its influences on the development of diabetic foot ulcers have not been investigated. The purpose of this study was to investigate the effect of diabetes on the biomechanical properties of plantar soft tissues and the relationship between biomechanical properties and plantar pressure distributions. We used the ultrasound indentation tests to measure force-deformation relationships of plantar soft tissues and calculate the effective Young's modulus and quasi-linear viscoelastic parameters to quantify biomechanical properties of plantar soft tissues. We also measured plantar pressures to calculate peak plantar pressure and plantar pressure gradient. Our results showed that diabetics had a significantly greater effective Young's modulus and initial modulus of quasi-linear viscoelasticity compared to non-diabetics. The plantar pressure gradient and biomechanical properties were significantly correlated. Our findings indicate that diabetes is linked to an increase in viscoelasticity of plantar soft tissues that may contribute to a higher peak plantar pressure and plantar pressure gradient in the diabetic foot.