Head Trauma

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and valid...

Zero-shot burned area mapping with the Segment Anything Model (SAM): a label-free framework for post-fire environmental assessment.

Accurate and rapid mapping of burned areas is critical for understanding the impacts of forest fires on ecosystems, the carbon cycle, and post-fire recovery processes. Traditional pixel-based approaches often provide limited accuracy due to spectral confusion, topographic variability, and sensitivity to empirically defined threshold values. Although deep learning frameworks such as U-Net, DeepLab,...

Deep learning for contrast-enhanced MRI in pediatric brain imaging.

PURPOSE: A deep learning algorithm for contrast amplification in brain MRI, trained exclusively on adult data, was tested for cross-population general...

Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury.

Exposure to trauma and mild traumatic brain injury (mTBI), which often co-occur and can both trigger acute and chronic pathophysiological processes, h...

Accelerated use of artificial intelligence-associated vocabulary in Japanese surgical journals after 2023.

PURPOSE: The increased use of artificial intelligence (AI)-based writing tools may influence academic writing styles. This study aimed to evaluate cha...

Minimizing chronic groin pain after inguinal hernia surgery: An augmented evidence review.

BACKGROUND: Chronic post-operative inguinal pain (CPIP) is a significant complication following inguinal hernia repair. Evidence on the surgical facto...

Dissecting interprofessional anatomy education: insights from AI-driven assessment.

BACKGROUND: In 2024, an 8-week IPE human anatomy dissection elective was offered to first-year students from various health professional programs. Pas...

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue...

Real-World Analysis of Organ Transplantation-Specific Agent Based on Large Language Model in Post-Transplant Self-Management During Off-Hours: A Mixed-Methods Study.

OBJECTIVE: A significant gap exists in medical support for organ transplant patients during out-of-hours (OOH). General large language models (LLMs), ...

Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science-A Scoping Review.

Post-stroke dysphagia (PSD) affects approximately 42% of acute stroke patients, increasing hospitalization costs and length of stay. Early identificat...

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers, with survival rates influenced by a variety of factors, including earl...

Impact of Deep Learning-Based Denoising on Image Quality and Diagnostic Confidence in Neurovascular Ultrahigh-Resolution Photon-Counting CT Angiography.

BACKGROUND AND PURPOSE: Ultrahigh-resolution (UHR) photon-counting detector (PCD)-CT angiography enables detailed visualization of the neurovasculatur...

An Aging Clock Based on Immune Repertoire Features: COVID-19 Accelerates Aging.

Aging induces immunosenescence, a progressive decline in immune function underpinning age-related pathogen vulnerability, yet T/B cell receptor (TCR/B...

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Brain image analysis presents significant challenges due to limitations in precision, computational efficiency, and interpretability. Although neural ...

Deep learning models based on post-procedural angiography for predicting the future risk of in-stent restenosis.

In-stent restenosis (ISR) remains a significant complication after percutaneous coronary intervention. Identifying patients at high risk for ISR from ...

AI-driven model for early failure prediction after HIFU integrating immediate post-ablation ultrasound and clinical data.

PURPOSE: High-Intensity Focused Ultrasound (HIFU) is an emerging focal therapy for localized prostate cancer, offering an alternative to radical prost...

[Value of a deep learning-based visual model for predicting postoperative upper limb functional recovery after severe acute cervical spinal cord injury].

Objective: To assess the value of a deep learning-based visual model for predicting postoperative upper limb functional recovery after severe acute ce...

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

BACKGROUND: Multicenter electronic health records (EHRs) can support quality improvement and comparative effectiveness research in critical care. Howe...

Somatic variant-calling beyond cancer: Repurposing algorithms to map low‑allele‑fraction variants across genomics.

BACKGROUND: Somatic variant callers were originally developed to identify tumour-specific mutations in mixed tumour-normal samples. Increasingly, disc...

Identifying subgroups of ICU patients with high mortality rates using machine learning: A nationwide, population-based study.

PURPOSE: Identifying subgroups of intensive care unit (ICU) patients with high mortality rates can provide directions in policy making about appropria...