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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

NeuroImage
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...

A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds.

Animal : an international journal of animal bioscience
Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification amo...

A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user's movement int...

A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

Ultrasound in medicine & biology
The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different...

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BMC bioinformatics
BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital mach...

Promoting head CT exams in the emergency department triage using a machine learning model.

Neuroradiology
PURPOSE: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage.

Joint optic disc and cup segmentation using semi-supervised conditional GANs.

Computers in biology and medicine
Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc...

Estimating treatment effects with machine learning.

Health services research
OBJECTIVE: To demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables).

Robot controlled, continuous passive movement of the ankle reduces spinal cord excitability in participants with spasticity: a pilot study.

Experimental brain research
Spasticity of the ankle reduces quality of life by impeding walking and other activities of daily living. Robot-driven continuous passive movement (CPM) is a strategy for lower limb spasticity management but effects on spasticity, walking ability and...