The rapid advancement of robotics technology in recent years has pushed the development of a distinctive field of robotic applications, namely robotic exoskeletons. Because of the aging population, more people are suffering from neurological disorders such as stroke, central nervous system disorder, and spinal cord injury. As manual therapy seems to be physically demanding for both the patient and therapist, robotic exoskeletons have been developed to increase the efficiency of rehabilitation therapy. Robotic exoskeletons are capable of providing more intensive patient training, better quantitative feedback, and improved functional outcomes for patients compared to manual therapy. This review emphasizes treadmill-based and over-ground exoskeletons for rehabilitation. Analyses of their mechanical designs, actuation systems, and integrated control strategies are given priority because the interactions between these components are crucial for the optimal performance of the rehabilitation robot. The review also discusses the limitations of current exoskeletons and technical challenges faced in exoskeleton development. A general perspective of the future development of more effective robot exoskeletons, specifically real-time biological synergy-based exoskeletons, could help promote brain plasticity among neurologically impaired patients and allow them to regain normal walking ability.
Percutaneous Coronary Intervention (PCI) with stent placement is a treatment effective for coronary artery diseases. Intravascular optical coherence tomography (OCT) with high resolution is used clinically to visualize stent deployment and restenosis, facilitating PCI operation and for complication inspection. Automated stent struts segmentation in OCT images is necessary as each pullback of OCT images could contain thousands of stent struts. In this paper, a deep learning framework is proposed and demonstrated for the automated segmentation of two major clinical stent types: metal stents and bioresorbable vascular scaffolds (BVS). U-Net, the current most prominent deep learning network in biomedical segmentation, was implemented for segmentation with cropped input. The architectures of MobileNetV2 and DenseNet121 were also adapted into U-Net for improvement in speed and accuracy. The results suggested that the proposed automated algorithm’s segmentation performance approaches the level of independent human observers and is feasible for both types of stents despite their distinct appearance. U-Net with DenseNet121 encoder (U-Dense) performed best with Dice’s coefficient of 0.86 for BVS segmentation, and precision/recall of 0.92/0.92 for metal stent segmentation under optimal crop window size of 256.
Background:
Valvular heart disease is a serious disease leading to mortality and increasing
medical care cost. The aortic valve is the most common valve affected by this disease.
Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the
images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic
Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks
(CNN) that can function optimally during a live echocardiographic examination for detection of the
aortic valve. An automated detection system in an echocardiogram will improve the accuracy of
medical diagnosis and can provide further medical analysis from the resulting detection.
Methods:
Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional
based Convolutional Neural Network (R-CNN) with various feature extractors were trained on
echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography
videos.
Results:
Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by
SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper-
second (fps) during real-time detection but managed to perform better than the other neural
network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing
Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all
models.
Conclusion:
Our findings provide a foundation for implementing a convolutional detection system
to echocardiography for medical purposes.
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