Neurology

Head Trauma

Latest AI and machine learning research in head trauma for healthcare professionals.

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Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediat...

TRIO-AI: Hybrid temporal graph, ODE, and VAE modeling for high-resolution cellular trajectory inference in liver injury

Resolving dynamic cellular transitions at single-cell resolution is essential for understanding complex biological processes in development, disease, and regeneration. However, existing trajectory inference methods struggle to capture heterogeneous temporal dynamics, particularly for rare or transitional cell populations critical to injury and repair responses. Here, we present TRIO-AI, a hybrid c...

AI-based Predictive Signaling Pathway Profiling in Cardiac Fibrosis Suggests a Novel Combinatorial Treatment Strategy

Cardiovascular disease (CVD) remains the leading cause of global mortality, with myocardial fibrosis characterized by excessive extracellular matrix (...

A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction

Genomic prediction and design require models that integrate local sequence features with long-range regulatory dependencies spanning hundreds of kilob...

A Machine Learning–3D Microvessel Platform Identifies Kinase Targets Restoring Blood-Brain-Barrier Endothelial Integrity

Disruption of the brain endothelial barrier is a hallmark of traumatic brain injury (TBI), and contributes to cerebral edema, coagulopathy, and delaye...

Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury

Early outcome prediction after acute traumatic spinal cord injury (SCI) is challenging due to pathological complexities and population heterogeneity. ...

Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biom...

Performance of an artificial intelligence foundation model for prostate radiotherapy segmentation

Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with min...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advan...

Automated Detection of Faciobrachial Dystonic Seizures Related Events in LGI1 Autoimmune Encephalitis Patients with Wearables

To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated w...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

Machine learning-based calculation of neurovascular compression surface area correlates with post-microvascular decompression pain outcomes for trigeminal neuralgia

Machine learning-generated segmentations of the trigeminal nerve and nearby blood vessels have the potential to quantify the magnitude of neurovascula...

High Sensitivity in Spontaneous Intracranial Hemorrhage Detection from Emergency Head CT Scans Using Meta-Learning Approach

Spontaneous intracranial hemorrhages have a high disease burden. Due to increasing medical imaging, new technological solutions for assisting in image...

Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review

Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem pr...

Post Induction Hypotension prediction during general anesthesia using Machine Learning Techniques

Intraoperative hypotension burden not equally distributed during various periods of a general anesthetic. Post-induction hypotension usually has an ia...

The Lyme Disease Controversy: An AI-Driven Discourse Analysis of a Quarter Century of Academic Debate and Divides

The scientific discourse surrounding Chronic Lyme Disease (CLD) and Post-Treatment Lyme Disease Syndrome (PTLDS) has evolved over the past twenty-five...

Clinical translation of ultrasoft Fleuron™ probes for stable, high-density, and bidirectional brain interfaces

Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training,...

RT-HaND-C: A Multi-Source, Validated Real-World Head and Neck Cancer Dataset for Research

Real-world data (RWD) is essential in head and neck cancer (HNC) research, offering insights into outcomes among diverse, comorbid patients often unde...

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, includin...

Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations

Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal t...

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