Head Trauma

Predicting 3D Post-Orthodontic Facial Outcomes With a Diffusion Model Trained on Unpaired Datasets.

BACKGROUND: Accurate prediction of facial aesthetics after orthodontic treatment is crucial for clinical planning, yet traditional methodologies often lack the required accuracy and realism. We introduce a novel generative artificial intelligence framework utilizing a diffusion model to predict patient-specific 3D facial morphology, specifically designed to function with unpaired pre- and post-tre...

Predicting infarct outcomes after extended time window thrombectomy in large vessel occlusion using knowledge guided deep learning.

BACKGROUND: Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data. METHODS: This retrospective study collected CT perfu...

Considerations for enhancing the clinical translational potential of LLM-Based TBI mortality prediction models.

This study explores the use of GPT-5 and traditional machine learning by scholars such as Tu et al. to predict the risk of emergency death in traumati...

Non-Neural Post-Thyroidectomy Dysphonia: Long-Term Voice Outcomes in 527 Patients.

OBJECTIVE: To characterize non-neural post-thyroidectomy dysphonia (PTD) by analyzing long-term voice outcomes in patients with no evidence of nerve i...

Identifying risk profile for adolescent e-cigarette use: A sex-stratified machine learning analysis.

INTRODUCTION: Recent studies show that young females now report higher e-cigarette use than males, reversing prior trends. While sex differences in us...

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-makin...

MedFusion-gP-AKI: development and multicenter validation of a machine learning fusion model for early prediction of KDIGO stage 3 acute kidney injury in critically ill traumatic cervicothoracic spinal cord injury patients.

KDIGO stage-3 acute kidney injury (AKI), a life-threatening complication in critically ill patients with traumatic cervicothoracic spinal cord injury ...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

HXMS: a standardized file format for HX-MS data.

MOTIVATION: Hydrogen/deuterium exchange-mass spectrometry (HX-MS) is a rapidly expanding technique used to investigate protein conformational ensemble...

Predicting Electrolyte Stability in Lithium and Post-Lithium Metal Batteries: The Failure of Linear Single Descriptor-Based Models.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the s...

Proof-of-concept: Differentiating upper trapezius muscle with myofascial trigger point using deep learning model on a small prospective sEMG dataset.

Myofascial pain syndrome commonly arises from myofascial trigger points (MTrPs). While needle electromyography (iEMG) reveals spontaneous activity at ...

Biodistribution of AAV-TT and AAV9 in the Nonhuman Primate Brain.

The adeno-associated virus (AAV) capsid variant known as "true type" (AAV-TT) is a capsid with enhanced central nervous system tropism and efficient r...

External validation of an AI-Based fracture detection tool for hip and pelvic radiographs in a multicenter retrospective cohort.

OBJECTIVE: Artificial intelligence tools show promise in fracture detection but may be impaired by hidden stratification. We aim to evaluate the diagn...

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic paramet...

Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.

BACKGROUND: Vancomycin-resistant Enterococcus (VRE) infection is a life-threatening complication after liver transplantation (LT). This study aimed to...

Unbiased quantification of persistent postural and motor deficits following spinal cord injury in mice.

Spinal cord injury (SCI) causes multifaceted postural and motor impairments that are challenging to quantify. Conventional behavioral tests, such as t...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to fals...

High MAP3K6 Expression in Spinal Cord Injury Tissues Enhances Neuronal Apoptosis: Insights from Multi-Omics Data Integrating WGCNA, Machine Learning, and Experimental Validation.

Spinal cord injury (SCI) is a major global health issue with severe complications, yet effective biomarkers remain elusive. We analyzed the GSE226238 ...

Computational frameworks for automated detection and quantification of paroxysmal sympathetic hyperactivity among traumatic brain injury patients.

Paroxysmal sympathetic hyperactivity (PSH) is a syndrome that occurs in a large subset of critically ill traumatic brain injury (TBI) patients and is ...