AIMC Topic: Stroke
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Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.
Health technology assessment (Winchester, England)
Mar 1, 2024
BACKGROUND: Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke.
[Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2024
In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation...
Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.
Radiology
Feb 1, 2024
Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To inv...
Effects of robot-assisted gait training on motor performance of lower limb in poststroke survivors: a systematic review with meta-analysis.
European review for medical and pharmacological sciences
Feb 1, 2024
OBJECTIVE: This study aimed to investigate the effects of robot-assisted gait training (RAGT) on improving walking ability, and to determine the optimal dosage of task-specific training based on RAGT for stroke patients.
Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.
Studies in health technology and informatics
Jan 25, 2024
According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-base...
Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (ICUs), where higher BPV correlates with increased mortality rates. Current interventions lack effecti...
Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.
NeuroRehabilitation
Jan 1, 2024
BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at...
Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.
NeuroRehabilitation
Jan 1, 2024
BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait t...
Machine learning-driven predictions and interventions for cardiovascular occlusions.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in...
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