Neurology

Robotic esophagectomy and 3-field lymphadenectomy with intraoperative nerve monitoring.

The standard of care for esophageal malignancies has evolved over the years from open transthoracic esophagectomy to a minimally invasive approach due to the reduction in surgical trauma and significant impact on postoperative outcomes. Minimally invasive approaches include video-assisted thoracoscopic surgery and robot-assisted thoracoscopic surgery. These minimally invasive approaches have an at...

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.

Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new ...

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.

In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated...

Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks.

The mapping of the time-dependent evolution of the human brain connectivity using longitudinal and multimodal neuroimaging datasets provides insights ...

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optima...

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental s...

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for ...

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular prot...

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosi...

Good view frames from ultrasonography (USG) video containing ONS diameter using state-of-the-art deep learning architectures.

This paper presents an automated method for detection of the diagnostically prominent frames containing optic nerve sheath (ONS) from ocular ultrasono...

Reversing the Ruin: Rehabilitation, Recovery, and Restoration After Stroke.

PURPOSE OF REVIEW: Stroke is a common cause of disability in aging adults. A given individual's needs after stroke vary as a function of the stroke ex...

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typicall...

Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy.

Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process ...

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify sp...

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for rel...

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.

Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that...

Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning.

Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical...

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry.

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosi...

Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging.

BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This s...

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, th...