Neurology

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection a...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to int...

Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy.

The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system an...

Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.

In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training...

Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis.

BACKGROUND AND AIMS: It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of strok...

Artificial intelligence in neuro-oncology.

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intel...

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage w...

Retracted: Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases.

[This retracts the article DOI: 10.1155/2022/3522510.].

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different ...

A thermodynamical model of non-deterministic computation in cortical neural networks.

Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high ac...

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting gra...

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem...

Generalizable synthetic MRI with physics-informed convolutional networks.

BACKGROUND: Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired cont...

A Woman with Bilateral Pheochromocytoma and Tuberous Sclerosis Complex.

BACKGROUND/OBJECTIVE: Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors. Here, we report an unusual case of synchronous PPGL in...

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads.

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To e...

Ultrasound-guided sciatic nerve block in Wistar rat cadavers: Relevant anatomy and injection distribution.

OBJECTIVE: To investigate the feasibility of an ultrasound-guided sciatic nerve block by describing the sonoanatomy and comparing the distribution of ...

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images.

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of...

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks.

BACKGROUND: It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces c...

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.

PURPOSE OF REVIEW: Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpr...

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. Howev...