Neurology

Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-indu...

3D Neuron Reconstruction in Tangled Neuronal Image With Deep Networks.

Digital reconstruction or tracing of 3D neuron is essential for understanding the brain functions. While existing automatic tracing algorithms work well for the clean neuronal image with a single neuron, they are not robust to trace the neuron surrounded by nerve fibers. We propose a 3D U-Net-based network, namely 3D U-Net Plus, to segment the neuron from the surrounding fibers before the applicat...

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.

BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces oppor...

Medical Diagnosis of Cerebral Palsy Rehabilitation Using Eye Images in Machine Learning Techniques.

Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (...

Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent ad...

Automated spectrographic seizure detection using convolutional neural networks.

PURPOSE: Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many ...

Applying Deep Neural Network Analysis to High-Content Image-Based Assays.

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerou...

Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and da...

Application of artificial neural network model in diagnosis of Alzheimer's disease.

BACKGROUND: Alzheimer's disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish...

Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals.

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study de...

ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins.

BACKGROUND: Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process t...

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demogr...

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using e...

Deep visual nerve tracking in ultrasound images.

Ultrasound-guided regional anesthesia (UGRA) becomes a standard procedure in surgical operations and pain management, offers the advantages of nerve l...

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease...

Learning-based classification of valence emotion from electroencephalography.

The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist...

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative pa...

A feature agnostic approach for glaucoma detection in OCT volumes.

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell w...

The Effect of Muscle Graft With Nerve Growth Factor and Laminin on Sciatic Nerve Repair in Rats.

INTRODUCTION: Peripheral nerve injury is one of the most common damages that lead to physical disability. Considering the similarity between the coati...

Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.

BACKGROUND: Given the increasing recognition of the significance of non-motor symptoms in Parkinson's disease, we investigate the optimal use of machi...