Artificial intelligence (AI) has been developed for echocardiography, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no o...
Although robotic telesurgery is growing in popularity, the benefits of telesurgery compared to local surgery are unclear. This study aimed to evaluate the performance of robotic tele-cholecystectomy with a commercial line using the Saroa (Riverfield,...
Journal of neuroengineering and rehabilitation
Sep 14, 2022
BACKGROUND: Robot-assisted gait training (RAGT) is a practical treatment that can complement conventional rehabilitation by providing high-intensity repetitive training for patients with stroke. RAGT systems are usually either of the end-effector or ...
OBJECTIVE: To evaluate the clinical effectiveness of micro-hand robot-assisted cholecystectomy (MRC) by comparing the clinical outcomes of patients with benign gallbladder disease treated with micro-hand or da Vinci robot-assisted cholecystectomy (DR...
OBJECTIVE: The aim of this study was to investigate the effects of EMG-driven robotic rehabilitation on hand motor functions and daily living activities of patients with acute ischemic stroke.
BACKGROUND: Combining robotic therapy (RT) with task-oriented therapy is an emerging strategy to facilitate motor relearning in stroke rehabilitation. This study protocol will compare novel rehabilitation regimens that use bilateral RT as a priming t...
Adherence to exercise programs for chronic low back pain (CLBP) is a major issue. The R-COOL feasibility study evaluated humanoid robot supervision of exercise for CLBP. Aims are as follows: (1) compare stretching sessions between the robot and a phy...
Abnormal spasticity and associated synergistic patterns are the most common neuromuscular impairments affecting ankle-knee-hip interlimb coordinated gait kinematics and kinetics in patients with hemiparetic stroke. Although patients with hemiparetic ...
BACKGROUND: Delineation of clinical target volume (CTV) for radiotherapy is a time-consuming and labor-intensive work. This study aims to propose a novel convolutional neural network (CNN)-based model for fast auto-segmentation of CTV. To evaluate it...
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