Head Trauma

Unravelling the brain resilience following stroke: From injury to rewiring of the brain through pathway activation, drug targets, and therapeutic interventions.

Synaptic plasticity is a neuron's intrinsic ability to make new connections throughout life. The morphology and function of synapses are highly susceptible to any pathological condition. Ischemic stroke is a cerebrovascular event that affects various brain regions, resulting in the loss of neural networks. Stroke can alter both structural and functional plasticity of synapses, leading to long-term...

Fast cortical thickness estimation using deep learning-based anatomy segmentation and diffeomorphic registration.

Accurately and efficiently estimating the cortical thickness from magnetic resonance images (MRIs) is crucial for neuroscientific studies and clinical applications with various large-scale datasets. Diffeomorphic registration-based cortical thickness estimation (DiReCT) is a prominent traditional method of calculating such measures directly from original MRIs by applying diffeomorphic registration...

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends

Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveragin...

Personalized translational medicine: Investigating YKL-40 as early biomarker for clinical risk stratification in hepatocellular carcinoma recurrence post-liver transplantation.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) presents a significant challenge, with recurrence rates ranging from 8% to ...

Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning

As machine learning systems increasingly rely on data subject to privacy regulation, selectively unlearning specific information from trained models...

EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization

Mixture-of-Experts (MoE) models have emerged as a cornerstone of large-scale deep learning by efficiently distributing computation and enhancing per...

Post Persona Alignment for Multi-Session Dialogue Generation

Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized respon...

Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements

Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor...

Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery

Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely presen...

Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health

This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient tes...

[EVALUATION OF SCREENING FOR PTSD USING CONSORT AI RECOMMENDATIONS WITH A LARGE LANGUAGE MODEL].

Similar to major natural disasters and large-scale wars, the events of October 7th and the retaliatory Iron Swords war resulted in both direct and ind...

Linking Symptom Inventories Using Semantic Textual Similarity.

An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variet...

Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model.

BACKGROUND: Acute kidney injury (AKI) is a critical complication in intensive care units (ICUs) that is known to have multifaceted impacts. However, a...

Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models

Large language models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal inform...

Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.

Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third m...

UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning

Unified multimodal large language models such as Show-o and Janus have achieved strong performance across both generation and understanding tasks. H...

A deep learning model for prediction of lysine crotonylation sites by fusing multi-features based on multi-head self-attention mechanism.

Lysine crotonylation (Kcr) is an important post-translational modification, which is present in both histone and non-histone proteins, and plays a key...

Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.

This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusi...

Genetic Influences on Brain Aging: Analyzing Sex Differences in the UK Biobank using Structural MRI

Brain aging trajectories differ between males and females, yet the genetic factors underlying these differences remain underexplored. Using structur...

Machine learning analysis of factors contributing to hypotension after lumbosacral epidural anaesthesia in dogs undergoing abdominal surgery.

The incidence of hypotension after a lumbosacral epidural in dogs depends on the volume of local anaesthetic administered. So far, there are no report...