Head Trauma

From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.

BACKGROUND: Artificial intelligence models for acute kidney injury (AKI) prediction achieve strong discriminative accuracy, yet clinical adoption remains constrained by model opacity and alarm fatigue. Explainable artificial intelligence (XAI) methods may enhance clinician trust and alert acceptance; however, the extent of their clinical validation and implementation remains unclear. METHODS: We p...

A four-gene signature for diagnosis of acute kidney injury following kidney transplantation.

Acute kidney injury (AKI) is a common and severe complication following renal transplantation, largely driven by ischemia-reperfusion injury (IRI). However, reliable biomarkers for post-transplant AKI remain unavailable. In this study, we analyzed transcriptomic datasets from multiple cohorts to identify robust gene signatures associated with transplant-related AKI. Weighted gene co-expression net...

Stem Cell Therapy for Traumatic Brain Injury: Translational Mechanisms, Biomarkers, and Clinical Trials.

Traumatic brain injury (TBI) is a leading cause of persistent cognitive, motor, and neuropsychiatric impairment, arising from both the initial mechani...

A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation.

OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...

Wearable sensor technology and machine learning for anterior cruciate ligament injury diagnosis: an exploratory feasibility study.

OBJECTIVES: To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration a...

Post-Pandemic Forecasting of Pediatric Acute Respiratory Infections with Deep Learning: A Multi-Pathogen, Multi-Time-Horizon Study.

BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity and hospitalization in children worldwide. In the post-COVID-19 era,...

A Multifactor Prediction Model of Recovery of Consciousness in Patients With Unresponsive Wakefulness Syndrome: A Multicenter, Retrospective Study.

AIMS: This multicenter retrospective cohort study aimed to develop 3 predictive models to estimate consciousness status 3 months after admission. Thes...

A donor-recipient ranking model to optimize long-term survival post liver transplant.

Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal...

Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.

BACKGROUND: Heart failure (HF) is a leading cause of hospitalization and readmission. Cardiac implantable electronic devices (CIEDs) continuously capt...

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is ...

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpo...

Factors associated with post-pornography masturbation among university students: a machine-learning study.

BACKGROUND: Pornography consumption is common among university students, yet little research has examined behavioral responses occurring immediately a...

Data After Death: Post-Mortem Ethics of Burn Patient Records and Images.

Burn care frequently relies on extensive documentation, including graphic photographic images and detailed clinical records. While these materials are...

A customized AI-based machine learning model for evaluating participants' activities under a workshop setting.

Human action recognition (HAR) in the workshop-style environment poses unique challenges due to class imbalance, fused action boundaries, and context ...

Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.

AIMS: Perioperative myocardial injury (PMI) is a frequent and often asymptomatic complication after non-cardiac surgery and is associated with increas...

Improving Trauma Triage Accuracy with Large Language Models: A Comparison to Human Expert Decisions.

BACKGROUND: Accurate prehospital trauma triage and communication determine morbidity, mortality, and system efficiency. Advancements in large language...

CDSegNet: a multi-scale convolution and attention mechanism U-shaped network for Crohn's disease lesion segmentation.

Accurate segmentation of Crohn's disease (CD) lesions from computed tomography enterography (CTE) cross-sectional images is crucial for diagnosing CD ...

Disparities in Engagement with an Artificial Intelligence-Based Chat Intervention Post-appendectomy.

PURPOSE: Artificial intelligence (AI)-based text messaging, or "chat," in post-appendectomy care has been shown to decrease preventable emergency depa...

Predicting long-term allograft outcomes in kidney transplant recipients using a machine learning approach: a 5-year retrospective cohort study.

Long-term outcomes of kidney allografts vary significantly among deceased donor kidney transplant recipients, and current prediction tools struggle to...

The entropic brain today.

Introduced in 2014 and revised in 2018, the entropic brain hypothesis has accrued a wealth of supportive evidence. The hypothesis states that-along a ...