AIMC Topic: Recovery of Function

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Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

BMC surgery
PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoper...

A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential i...

A Case Report of Becker Muscular Dystrophy and Stroke Who Successfully Regained Mobility With Robot-Assisted Gait Training.

American journal of physical medicine & rehabilitation
A 30-yr-old patient with Becker muscular dystrophy presented with stroke. Background issues of proximal weakness, dilated cardiomyopathy, and reduced endurance challenged the usual goal-setting and formulation of a stroke rehabilitation plan. We disc...

Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis.

Annals of medicine
BACKGROUND: Robot-assisted upper-limb rehabilitation has been studied for many years, with many randomised controlled trials (RCTs) investigating the effects of robotic-assisted training on affected limbs. The current trend directs towards end-effect...

Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a SAR can lead to an improvement in the functional ability of individuals post-stroke. The aim of this...

Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience.

Journal of neuroengineering and rehabilitation
BACKGROUND: Unsupervised robot-assisted rehabilitation is a promising approach to increase the dose of therapy after stroke, which may help promote sensorimotor recovery without requiring significant additional resources and manpower. However, the un...

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only u...

Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm.

Orthopaedics & traumatology, surgery & research : OTSR
BACKGROUND: An important aim of total knee arthroplasty is to achieve functional recovery, which includes post-operative increase in walking speed. Therefore, predicting whether a patient will walk faster or slower after surgery is important in TKA, ...

The Combined Effect of Robot-assisted Therapy and Activities of Daily Living Training on Upper Limb Recovery in Persons With Subacute Stroke: A Randomized Controlled Trial.

Archives of physical medicine and rehabilitation
OBJECTIVES: To evaluate the effectiveness of robot-assisted therapy (RAT) followed by activities of daily living (ADL) training in comparison with conventional rehabilitation therapy (CRT) and ADL training in individuals with subacute stroke.