Neurology

Head Trauma

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

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Showing 883-903 of 6,597 articles
Machine Learning-Based Modeling for Predicting Hypopituitarism After Cranial Trauma.

OBJECTIVE: Traumatic brain injury (TBI) remains a leading cause of disability and death globally, an...

May 2025 40368087
Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at me...

May 2025 40355690
Hair Transplantation: State of the Art.

BACKGROUND: Hair transplantation is a minimally invasive procedure for treating hair loss in men and...

May 2025 40354670
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 systemi...

May 2025 40321247
Assessing the impact of the TB response in Taiwan - the journey towards ending TB.

The incidence of TB in Taiwan declined by 62% from 2005 to 2023 (i.e., from 73/100,000 to 28/100,000...

(2)
May 2025 40365028
The effects of the post-delay epochs on working memory error reduction.

Accurate retrieval of the maintained information is crucial for working memory. This process primari...

May 2025 40359421
Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects pat...

May 2025 40418113
Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients aft...

May 2025 40310715
TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments

Objectives: While Large Language Models (LLMs) have been widely used to assist clinicians and supp...

How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues

The growing adoption of synthetic data in healthcare is driven by privacy concerns, limited access...

In defence of post-hoc explanations in medical AI

Since the early days of the Explainable AI movement, post-hoc explanations have been praised for t...

Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations

The advancement of AI systems for mental health support is hindered by limited access to therapeut...

A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images

Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Im...

Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations

Post-hoc explanation methods provide interpretation by attributing predictions to input features. ...

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Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting oppo...

(2)
Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient manag...

DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers

Text-to-image generation models, especially Multimodal Diffusion Transformers (MMDiT), have shown ...

(2)
Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation

Code translation migrates codebases across programming languages. Recently, large language models ...

Improving Quantization with Post-Training Model Expansion

The size of a model has been a strong predictor of its quality, as well as its cost. As such, the ...

Post-composing ontology terms for efficient phenotyping in plant breeding.

Ontologies are widely used in databases to standardize data, improving data quality, integration, an...

(1)
Mar 2025 40117331
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