Head Trauma

Radiography-based AI decision support for further post-traumatic knee MRI referral in children.

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learning (ML) model using clinical and laboratory data to predict PSS risk in acute ischemic stroke (AIS) patients post-thrombolysis, aiming to enhance risk assessment and clinical management. Retrospective analysis included 332 AIS patients treated betwee...

A fast fully automated approach for evaluating calcified lesions in intravascular ultrasound.

AIMS: Catheter-based coronary intervention is an effective treatment for acute coronary syndrome. However, calcified plaques pose significant challeng...

Prior beliefs & automated fact checking: Limits on the effectiveness of AI-based corrections.

Proliferation of misinformation poses significant challenges in contemporary society, necessitating efficient strategies for its identification and mi...

Pioneering the future: Delivering the first digitally generated CMC post-approval change dossier to global regulators simultaneously.

As medicinal products and their manufacturing processes evolve, biopharmaceutical companies must continuously manage, document, and submit post-approv...

Tailored psychotherapy and AI-enhanced contingency management for co-occurring disorders in cannabis use disorder: a systematic review.

BACKGROUND: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hype...

Exploratory identification of intestinal health and productive performance patterns in post-weaning piglets using explainable machine learning.

Weaning is a critical stage in swine production, characterized by intestinal alterations that affect piglet health and performance. In this study, mac...

Prognostic Significance of Lymph Node Ratio for Overall Survival in Patients with Lymph Node-Positive Bladder Cancer Post-Radical Cystectomy and Chemotherapy: A Multicenter Cohort Analysis.

BACKGROUND: The lymph node ratio (LNR) is gaining recognition as a prognostic biomarker for various malignant neoplasms. However, its prognostic role ...

Post-secondary student adoption of mental health labels through their use of social media: a scoping review.

BACKGROUND: Social media is a significant source of information for post-secondary students, who are usually at the age at which many common mental di...

Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.

BACKGROUND: In adolescents, identifying objective biomarkers for treatment response is crucial for the development of effective interventions. Voice-b...

Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.

OBJECTIVES: Early recognition of individuals at elevated risk for new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is vita...

Artificial intelligence risk stratification from dynamic digital subtraction angiography radiomics predicts pulmonary embolism and associates with clinical outcomes in deep vein thrombosis: A retrospective cohort study.

OBJECTIVE: Current risk stratification for lower extremity deep vein thrombosis remains limited, often failing to identify high-risk patients for impe...

Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach.

BACKGROUND: Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, ar...

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.

BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improv...

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography.

OBJECTIVE: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to c...

A Shape and Size-Scaled Deep Learning Brain Injury Model for Near Real-Time Dynamic Impact Simulation.

Efficient deep learning brain injury models enable large-scale, strain-based investigations of traumatic brain injury (TBI). Here, we extend a previou...

Comprehensive benchmarking of deep learning approaches for automated astrocyte segmentation in traumatic brain injury.

Astrocyte morphological changes and GFAP upregulation are hallmarks of traumatic brain injury (TBI) and quantifying these alterations in tissues is es...

Monitoring the lateral ventricles in the presence of intracranial hemorrhage using automated dual segmentation.

BACKGROUND: Segmentation of intracranial hemorrhage (ICH) alongside the brain's ventricles can provide crucial information in the management stroke or...

Explainable machine learning model for predicting traumatic brain injury-induced coagulopathy in elderly patients: A multicenter cohort study.

BACKGROUND: Traumatic brain injury-induced coagulopathy (TBI-IC) in the elderly is a severe complication of traumatic brain injury (TBI) that leads to...

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation.

OBJECTIVES: To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruct...