Refractive Surgery

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 vital for planning effective preventive interventions. The aim of this study was to develop a deep learning (DL) radiomics model to predict NIILs post-CAS from dual-energy CT (DECT) images. MATERIALS AND METHODS: This study retrospectively enrolled patie...

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 impending pulmonary embolism (PE) and leading to non-guideline-concordant overtreatment. We aimed to develop and validate a novel artificial intelligence (AI) system that processes dynamic digital subtraction angiography (DSA) radiomics, with the potenti...

3D-QALAS synthetic MRI with Zero-DeepSub in children: initial experience including post-contrast imaging feasibility.

BACKGROUND: Magnetic resonance imaging (MRI) in children requires multiple sequences, leading to lengthy exams and motion-related challenges. Syntheti...

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...

Convolutional neural network-derived neurofibrillary tangle classifiers: Investigative tools to identify maturation levels and explore post-translational modifications using laser capture microdissection coupled mass spectrometry.

Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal flu...

Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care.

BACKGROUND AND SIGNIFICANCE: Ambient listening tools powered by generative artificial intelligence (GenAI) offer real-time, scribe-like support that r...

A Multimodal Multitask Artificial Intelligence Model for Orthokeratology Contact Lens Fitting: An Integrated Framework to Enhance Lens Centration and Myopia Control Effect.

PURPOSE: To develop a multimodal, multitask artificial intelligence (AI) model to classify postfitting corneal topography patterns and predict axial l...

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...

Comparison of Pragmatic Post-Thrombectomy Prognostic Scores Not Based on Advanced Imaging in a Large National Stroke Registry.

BACKGROUND: Nearly half of the patients who received endovascular thrombectomy (EVT) for large vessel occlusion experience poor functional outcomes. R...

Extracellular matrix stiffness drives post-mitotic nuclear pore complex assembly to promote neuroblastoma pathogenesis.

Nuclear pore complex (NPC) undergoes dynamic changes in physiology and pathology, yet its roles in neuroblastoma (NB) remain unclear. We demonstrated ...

Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study.

BACKGROUND: The month of June has been recognized as the National Cytomegalovirus (CMV) Awareness Month since 2011 in the United States. Established b...

Machine learning-enabled prediction of hemorrhagic transformation post-thrombectomy using quantitative DSA.

This study aims to construct a predictive model for post-thrombectomy hemorrhagic transformation (HT) by integrating hemodynamic features derived from...

The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.

AIMS: Focused cardiac ultrasound (FoCUS) can yield valuable information for decision-making. However, it is limited by the skills required to acquire ...

Correction to: Deep learning prediction of steep and flat corneal curvature using fundus photography in post‑COVID telemedicine era.

Leveraging an AI-Driven Voice Companion to Enhance Mental Health in Older Adults in Post-Acute and Long-Term Care Settings: A Feasibility Study.

Loneliness, depression, and anxiety are prevalent among older adults in post-acute and long-term care settings and are associated with adverse health ...

Investigating the relationship between teacher efficacy, job satisfaction, and digital resource utilization in assessment practices: Insights from PISA 2018 and 2022.

The COVID-19 pandemic disrupted global education systems, forcing rapid shifts in teaching practices, technology integration, and assessment methods. ...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

Predicting functional results of percutaneous coronary intervention using machine learning modelling.

BACKGROUND: Post-percutaneous coronary intervention (PCI) Murray's law-based quantitative flow ratio (μFR) is associated with long-term clinical outco...

To see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditions.

Barriers to accessing veterinary-care for dog-owners are diverse and dynamic, and widely accepted as major canine welfare threats because of potential...

Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications.

CONTEXT: Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit...