Head Trauma

Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa.

BACKGROUND: Unplanned and potentially avoidable readmission within 30 days post discharge is a major financial burden.

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retrospectively collected from three specialist centres in China. Pre- and post-operative variables were selected using support vector machine, random fores...

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging a...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance im...

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications.

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. ...

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In...

Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncyclo...

ADFQ-ViT: Activation-Distribution-Friendly post-training Quantization for Vision Transformers.

Vision Transformers (ViTs) have exhibited exceptional performance across diverse computer vision tasks, while their substantial parameter size incurs ...

AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS...

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.

BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prol...

A novel generative model for brain tumor detection using magnetic resonance imaging.

Brain tumors are a disease that kills thousands of people worldwide each year. Early identification through diagnosis is essential for monitoring and ...

Machine Learning Approaches to Prognostication in Traumatic Brain Injury.

PURPOSE OF REVIEW: This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It undersco...

Identifying plastic materials in post-consumer food containers and packaging waste using terahertz spectroscopy and machine learning.

Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value...

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective st...

Utilization of machine learning algorithm in the prediction of rehospitalization during one-year post traumatic spinal cord injury.

STUDY DESIGN: Retrospective cohort study.

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with...

Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging.

Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral ima...

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS...

Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.

BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-indu...

Using machine learning to predict outcomes following transcarotid artery revascularization.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complicat...