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Predictors of activities of daily living outcomes after upper limb robot-assisted therapy in subacute stroke patients.

PloS one
BACKGROUND: Upper limb recovery is one of the main goals of post-stroke rehabilitation due to its importance for autonomy in Activities of Daily Living (ADL). Although the efficacy of upper limb Robot-assisted Therapy (RT) is well established in lite...

Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVE: Sudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. This study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the be...

Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices.

Journal of neuroengineering and rehabilitation
BACKGROUND: End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient's hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic conf...

Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing.

IEEE transactions on bio-medical engineering
OBJECTIVE: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors to monitor swallowing function during an individual's daily life.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Academic radiology
RATIONALE AND OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV) not explained by other causes. W...

Maternal exposure to ambient PM during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models.

The Science of the total environment
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM) on CHDs are inconsistent. We used ...

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Nature biomedical engineering
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be di...

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suf...

Safety and Feasibility of a Novel Active Fixation Temporary Pacing Lead.

The Journal of invasive cardiology
OBJECTIVE: This first-in-human study evaluated the safety and technical feasibility of the Tempo temporary cardiac pacing lead (BioTrace Medical), which includes a novel fixation mechanism and soft tip.

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

Computers in biology and medicine
Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the u...