Neurology

Automatic Tissue Differentiation in Parotidectomy using Hyperspectral Imaging

In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of $400-1000$ nm is used in this work. The acquisit...

Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despi...

Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.

Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. However, it did not acquire enough attention. Less ...

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a ...

DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining

Glaucoma is a progressive optic neuropathy characterized by structural damage to the optic nerve head and functional changes in the visual field. De...

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data

Segmenting stroke lesions in Magnetic Resonance Imaging (MRI) is challenging due to diverse clinical imaging domains, with existing models strugglin...

MACAW: A Causal Generative Model for Medical Imaging

Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in...

STORM: Strategic Orchestration of Modalities for Rare Event Classification

In domains such as biomedical, expert insights are crucial for selecting the most informative modalities for artificial intelligence (AI) methodolog...

An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques

This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques...

Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks.

Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify ...

Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis

Recent works in pathological speech analysis have increasingly relied on powerful self-supervised speech representations, leading to promising resul...

Topological analysis of brain dynamical signals indicates signatures of seizure susceptibility

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (non-ictal) brain dynamics rema...

3D Spine Shape Estimation from Single 2D DXA

Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging pla...

CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

PURPOSE: Optical coherence tomography (OCT)-derived measurements of the optic nerve head (ONH) from different devices are not interchangeable. This po...

Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Data

The increasing global aging population necessitates improved methods to assess brain aging and its related neurodegenerative changes. Brain Age Gap ...

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep...

Accuracy and safety evaluation of a novel artificial intelligence-based robotic system for autonomous spinal posterior decompression.

OBJECTIVE: This study aimed to introduce a novel artificial intelligence (AI)-based robotic system for autonomous planning of spinal posterior decompr...

An evolving machine-learning-based algorithm to early predict response to anti-CGRP monoclonal antibodies in patients with migraine.

BACKGROUND: The present study aimed to determine whether machine-learning (ML)-based models can predict 3-, 6, and 12-month responses to the monoclona...

Prognostic Effect of Trigeminal Neuralgia Treated With Percutaneous Balloon Compression by Machine Learning-based Modeling of Radiomic Morphological Features.

BACKGROUND: Trigeminal neuralgia (TN) is defined as spontaneous pain in the region of the trigeminal nerve that seriously affects a patient's quality ...