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Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction.

Scientific reports
Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory responses. Prefrontal cortex (PFC) has been implicated in drug addiction. However, the mechanism underlying...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Genomics, proteomics & bioinformatics
Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by ...

CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.

Medical physics
PURPOSE: Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy...

Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation.

Future cardiology
Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study assessed the potential of machine-learning algorithms to detect NVAF. A retrospective database study u...

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have g...

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

Sensors (Basel, Switzerland)
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of op...

Effectiveness of a chat-bot for the adult population to quit smoking: protocol of a pragmatic clinical trial in primary care (Dejal@).

BMC medical informatics and decision making
BACKGROUND: The wide scale and severity of consequences of tobacco use, benefits derived from cessation, low rates of intervention by healthcare professionals, and new opportunities stemming from novel communications technologies are the main factors...

Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.

Medical image analysis
Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is lab...

How people with dementia perceive a therapeutic robot called PARO in relation to their pain and mood: A qualitative study.

Journal of clinical nursing
BACKGROUND: Interacting with social robots, such as the robotic seal PARO, has been shown to improve mood and acute pain for people with dementia. Little attention has been paid to the effect of PARO on people with dementia and chronic pain.

Two Distinct Neural Timescales for Predictive Speech Processing.

Neuron
During speech listening, the brain could use contextual predictions to optimize sensory sampling and processing. We asked if such predictive processing is organized dynamically into separate oscillatory timescales. We trained a neural network that us...