Latest AI and machine learning research in strokes for healthcare professionals.
BACKGROUND AND OBJECTIVE: Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep learning model.
BACKGROUND AND PURPOSE: One-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories' proportions are unknown.
The effects of caregiver burden during the inpatient rehabilitation period have not yet been investigated. The purpose of this study was to evaluate t...
PURPOSE: There is limited evidence of gait training using newly developed exoskeletal lower limb robot called Hybrid Assistive Limb (HAL) on the funct...
Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The s...
BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The...
Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize motor recovery following stroke. However, few studies ...
BACKGROUND: Although the Gait Exercise Assist Robot (GEAR) has been reported to effectively improve gait of hemiplegic patients, no study has investig...
Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized cl...
BACKGROUND: To date, the missed diagnosis rate of pulmonary hypertension (PH) was high, and there has been limited development of a rapid, simple, and...
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a ...
Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 ho...
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in ...
Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other c...
BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h of stroke onset. Quick and precise identification o...
The use of artificial intelligence in numerous prediction and classification tasks, including clinical research and healthcare management, is becoming...
BACKGROUND: This study aimed to establish and validate a machine learning-based model for the prediction of early phase postoperative hypertension (EP...
BACKGROUND: Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, ech...
Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main...
The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a R...