AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Neural Conduction

Showing 1 to 10 of 10 articles

Clear Filters

Self-organization of a recurrent network under ongoing synaptic plasticity.

Neural networks : the official journal of the International Neural Network Society
We investigated the organization of a recurrent network under ongoing synaptic plasticity using a model of neural oscillators coupled by dynamic synapses. In this model, the coupling weights changed dynamically, depending on the timing between the os...

Towards a predictive model for Guillain-Barré syndrome.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The severity of Guillain-Barré Syndrome (GBS) varies among subtypes, which can be mainly Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN) and Miller-Fisher Syn...

Chaos versus noise as drivers of multistability in neural networks.

Chaos (Woodbury, N.Y.)
The multistable behavior of neural networks is actively being studied as a landmark of ongoing cerebral activity, reported in both functional Magnetic Resonance Imaging (fMRI) and electro- or magnetoencephalography recordings. This consists of a cont...

A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs.

Journal of computational neuroscience
Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Jo...

Machine learning-based approach for disease severity classification of carpal tunnel syndrome.

Scientific reports
Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables eac...

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.

Physiological reports
Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of mus...

Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy.

Ultrasound in medicine & biology
Recently, deep learning (DL) algorithms have been adapted for the diagnosis of medical images. The purpose of this study was to detect image features using DL without measuring median nerve cross-sectional area (CSA) in ultrasonography (US) images of...

Diagnosis of carpal tunnel syndrome using a 10-s grip-and-release test with video and machine learning analysis.

The Journal of hand surgery, European volume
UNLABELLED: We developed a finger motion-based diagnostic system for carpal tunnel syndrome by analysing 10 second grip-and-release test videos. Using machine learning, it estimated presence of carpal tunnel syndrome (89% sensitivity and 83% specific...

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning.

Ultrasound in medicine & biology
OBJECTIVE: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating thi...

Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes.

Neurophysiologie clinique = Clinical neurophysiology
OBJECTIVE: The objective of this study was to develop artificial intelligence-based deep learning models and assess their potential utility and accuracy in diagnosing and predicting the future occurrence of diabetic distal sensorimotor polyneuropathy...