Head Trauma

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used i...

Enhancing neural stem cell integration in the injured spinal cord through targeted PTEN modulation.

JOURNAL/nrgr/04.03/01300535-202604000-00039/figure1/v/2025-06-30T060627Z/r/image-tiff Spinal cord injury results in permanent loss of neurological functions due to severance of neural networks. Transplantation of neural stem cells holds promise to repair disrupted connections. Yet, ensuring the survival and integration of neural stem cells into the host neural circuit remains a formidable challeng...

Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.

BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post...

Robot-Assisted Endodontic Retreatment: A Case Report with Clinical Considerations.

Fiber posts present significant challenges for nonsurgical endodontic retreatment, as improper removal may result in iatrogenic root perforation or ev...

Plasma IL-17A is increased in patients with critical MIS-C and associated to in-hospital mortality.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe post-COVID-19 complication with multiple phenotypes.

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.

BACKGROUND: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single react...

Predicting work ability impairment in post COVID-19 patients: a machine learning model based on clinical parameters.

The Post COVID-19 condition (PCC) is a complex disease affecting health and everyday functioning. This is well reflected by a patient's inability to w...

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. De...

Changes in hematoma volume following aneurysmal subarachnoid hemorrhage and its impact on patient prognosis.

OBJECTIVE: This study aims to investigate the effects of preoperative intracerebral hematoma volume (HVpre), hematoma volume 6-8 days post-surgery (HV...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learnin...

Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets.

Packed columns are commonly used in post-combustion processes to capture CO emissions by providing enhanced contact area between a CO-laden gas and CO...

A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.

OBJECTIVE: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neur...

Current trends in pharmaceutical industry: Post -CoVid-19 pandemic effects.

The pharmaceutical industry is undergoing a period of significant transformation. This is driven by advances in technology, increased regulatory scrut...

Prediction of prognosis in patients with cerebral contusions based on machine learning.

Traumatic brain injury (TBI) is a global issue and a major cause of patient mortality, and cerebral contusions (CCs) is a common primary TBI. The haem...

Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by tradition...

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to addres...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threate...

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.

BACKGROUND: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and developmen...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant chal...