Head Trauma

Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.

Early identification and prevention of persistent acute kidney injury (pAKI) remain challenging due to delayed biochemical markers and limited tools to differentiate between transient and persistent forms. We retrospectively analyzed data of 2,285 patients who underwent cardiac surgery with cardiopulmonary bypass (CPB) and developed 3 machine learning (ML) models for predicting pAKI: model 1 (preo...

Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

BACKGROUND: Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with acute ischemic stroke, but it remains unclear whether those markers can predict post-transfer core volume. METHODS: We performed a post-hoc analysis of prospectively enrolled patients transferred from a primary to a comprehensive center (PSC/CSC) fo...

Vocal Biomarkers of Childhood Trauma: A Machine-Learning Approach to Speech Analysis.

PURPOSE: Childhood trauma can disrupt communication, yet early signs often go unrecognized in regions affected by ongoing war, where immediate physica...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

A Randomised Study of ChatGPT-4o vs. Written Materials for Teaching Periodontal-Systemic Relationships in Dental Education.

INTRODUCTION: The bidirectional relationship between periodontal and systemic diseases (particularly diabetes and cardiovascular diseases) is central ...

Pediatric Firearm Risk Prediction in Trauma Centers and After Discharge: A Machine Learning Analysis.

OBJECTIVE: We used machine learning (ML) to develop firearm risk prediction models for injured children and adolescents admitted to U.S. trauma center...

A Multimodal deep learning method for predicting drug-induced liver injury using structural and sequential molecular representations.

Drug-induced liver injury (DILI) remains one of the leading causes of drug development failure and post-marketing withdrawal. Despite recent advances ...

Reducing breath-hold time in liver MRI: Clinical performance of deep learning-accelerated post-contrast T1 VIBE sequences.

BACKGROUND: Post-contrast liver MRI often requires long breath-holds, risking motion artifacts that can reduce diagnostic quality. We assessed whether...

Establishment of a novel deep learning-based method for gait analysis after peripheral nerve injury in pigs.

BACKGROUND: Peripheral nerve injury with deficits has poor functional prognosis, making motor function assessment during nerve regeneration crucial. R...

A field boundary dataset for the canadian prairies derived from sentinel-2 imagery using the segment anything model.

This article presents a Prairie-wide spatial vector dataset of agricultural field boundaries across Alberta, Saskatchewan, and Manitoba, Canada. The d...

Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence imaging.

BACKGROUND: The adult mammalian cerebral cortex has a vertical laminar organization consisting of six neuronal layers, with each layer subserving a sp...

Rapid earthquake damage assessment via hybrid LSTM-RNN with a quantum-inspired classification head based on Autonomous Perceptron Model APM.

Rapid post-event assessment of earthquake damage is essential for resilient emergency response and risk mitigation. We present a multi-scenario deep l...

Profiling of hypoxic and extracellular matrix remodeling markers in biofluids and cerebral tissue after traumatic brain injury: expression and signaling crosstalk of HIF-1α, MMP-9/14 and EPHA2.

Traumatic brain injury (TBI) remains a formidable clinical, neuropathological, and forensic challenge, constituting a leading cause of death as well a...

Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net.

Neoadjuvant therapy (NAT) has demonstrated considerable effectiveness in treating locally advanced non-small cell lung cancer (NSCLC). Major pathologi...

Past, present and future perspectives of traumatic brain injury.

Despite extensive research, traumatic brain injury (TBI) imposes significant mortality and morbidity burden globally. This narrative review provides a...

Developing a practical machine learning model to predict post implantation syndrome after endovascular aneurysm repair.

BACKGROUND: Post-implantation syndrome (PIS) is recognized as a systemic inflammatory response following endovascular aneurysm repair (EVAR), characte...

Application of machine learning in the research progress of post-kidney transplant rejection.

Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs. With the rapid adv...

AI-enhanced precision: boosting pathologist confidence in HER2 ultra-low diagnosis through intelligent re-screening.

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) immunohistochemistry (IHC) expression, particularly the precise identification ...

Leveraging artificial intelligence to accelerate competency development in diagnostic reasoning of medical students.

INTRODUCTION: Diagnostic errors remain a critical patient safety concern, yet teaching diagnostic reasoning is challenging due to its complex, context...

Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification.

Multi-element alloy catalysts exhibit tunable electronic structures and remarkable thermal stability, making them promising materials for automotive e...