Head Trauma

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humor (VH) is an avascular tissue between the lens and the retina, mainly composed by a collagen fibers network, hyaluronic acid, and hyalocytes. Recently...

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutica...

One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study.

BACKGROUND: Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work.

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, ...

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes....

Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video.

BACKGROUND AND OBJECTIVES: Post-traumatic stress disorder is a debilitating psychological condition that can manifest following exposure to traumatic ...

Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury.

BACKGROUND: In neurointensive care, increased intracranial pressure (ICP) is a feared secondary brain insult in traumatic brain injury (TBI). A system...

Effect of ethanol extract of nigella sativa L seeds and propofol on BDNF protein level as neuroplasticity and neuroprotection of traumatic brain injury in rats.

BACKGROUND: Traumatic brain injury (TBI) is a change in brain function or evidence of brain pathology caused by external mechanical forces. Brain Deri...

Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network.

OBJECTIVE: This study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and constr...

A deep learning-based approach for unbiased kinematic analysis in CNS injury.

Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the ...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review a...

Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study.

BACKGROUND: Currently, the treatment and care of patients with traumatic brain injury (TBI) are intractable health problems worldwide and greatly incr...

Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.

BACKGROUND: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological d...

Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis.

Computed tomography (CT) is used as a valuable tool for device selection for interventional therapy in tricuspid regurgitation (TR). We aimed to evalu...

Dynamic changes in pyroptosis following spinal cord injury and the identification of crucial molecular signatures through machine learning and single-cell sequencing.

The pathological cascade of spinal cord injury (SCI) is highly intricate. The onset of neuroinflammation can exacerbate the extent of damage. Pyroptos...

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory sy...

PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and ...

AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction.

OBJECTIVE: Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human ...

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients usin...

Diagnostic evaluation of blunt chest trauma by imaging-based application of artificial intelligence.

Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and ev...