Radiology

Sex differences in plaque burden and outcomes in symptomatic patients with suspected coronary artery disease.

BACKGROUND: Sex-related differences in coronary artery disease (CAD) burden and outcomes are increasingly recognized but not fully understood, particularly when assessed using advanced imaging techniques. OBJECTIVES: To investigate sex differences in coronary plaque characteristics and their association with long-term cardiovascular outcomes in symptomatic patients undergoing coronary computed tom...

Illuminating Research Dynamics: Medical Ultrasound and Deep Learning.

BACKGROUND: Deep learning (DL) has enabled advances in ultrasound imaging, but challenges like limited datasets and device variability hinder progress. This study provides the first bibliometric overview of DL research in medical ultrasound. METHOD: We retrieved related publications (2004-Apr 2025) on medical ultrasound and DL from the Web of Science Core Collection. Bibliometric tools (Bibliometr...

Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.

PURPOSE: To develop and validate a machine learning model that integrated MRI radiomics features and clinical factors for preoperative prediction of p...

Artificial Intelligence-Assisted Indocyanine Green Angiography for Perforators Identification in the Anterolateral Thigh Flap.

BACKGROUND: The anterolateral thigh (ALT) flap is widely used for head and neck reconstruction because of its versatility and reliable vascular supply...

ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U-Nets.

PURPOSE: To propose a spatio-temporal U-Net based network (4DST) that exploits both spatial and dynamic information while avoiding memory-intensive 4D...

Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning.

PURPOSE: This work proposes a novel self-supervised noise-adaptive image denoising framework, called Repetition to Repetition (Rep2Rep) learning, for ...

Integrative Machine Learning Model Leveraging DCE-MRI and PSA Values for Advanced Risk Stratification in Prostate Cancer.

PURPOSE: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learni...

Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT.

OBJECTIVES: 1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (Dmax) and volumes of aortic dissect...

A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.

Autism Spectrum Disorder (ASD) is one of the most common neurodevelopmental disorders affecting patients from childhood to adulthood. Yet, its patholo...

Digital twins and simulations in transcatheter coronary and structural heart interventions.

Digital twin technology, which enables the creation of patient-specific virtual models, is increasingly applied in interventional cardiology to suppor...

Mapping systemic inter-organ metabolic networks across glycemic continuum using whole-body [18F]FDG PET/CT and machine learning.

PURPOSE: Glucose homeostasis relies on coordinated interactions among multiple organs, and its disruption relates to diabetes development. This study ...

Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging.

RATIONALE AND OBJECTIVES: To assess the impact of a deep learning-based noise reduction (DLD) technique on image quality and diagnostic accuracy for t...

Deep learning approach for DCE-MRI parameter estimation: Evaluating signal intensity and concentration-time curve-based convolution-neural-networks.

Dynamic contrast-enhanced MRI (DCE-MRI) is common technique for assessing tissue perfusion and permeability in brain tumors (e.g., gliomas), using gen...

Deep learning model using contrast-enhanced CT for predicting overall survival in oropharyngeal squamous cell carcinoma: a prospective multicenter study.

BACKGROUND: Accurate risk stratification for overall survival (OS) in patients with oropharyngeal squamous cell carcinoma (OPSCC) is critical for guid...

Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.

INTRODUCTION AND OBJECTIVES: An AI model that performs well during training does not guarantee similar performance in clinical practice and should be ...

Assessment of deep learning reconstruction effects on detection and differentiation of liver metastasis from hepatic hemangioma in diffusion-weighted imaging.

PURPOSE: To evaluate and compare the performance of diffusion-weighted imaging (DWI) using compressed sensing (CS) and DWI using CS with model-based d...

Predicting hydrophone phase and evaluating its uncertainty using magnitude data and minimum phase approach.

Hydrophones are commonly used to measure the acoustic output of ultrasound transducers and devices. Commonly, only the magnitude response of a hydroph...

The Application of Magnetic Resonance Imaging in Dentistry: A Bibliometric Analysis.

OBJECTIVES: The aim of this analysis was to investigate the historical development, current status, and research hotspots related to the application o...

An Ultrasound-Based Machine Learning Model for Differentiating IgG4-Related Sialadenitis and Primary Sjögren's Syndrome.

INTRODUCTION AND AIMS: To establish and validate an interpretable machine learning (ML) model based on ultrasound (US) scoring system for differentiat...

Speckle Noise Reduction Techniques in Ultrasound Imaging: A comprehensive review of the last two decades (2005-2024).

Ultrasound imaging has become a widely used medical modality over the past few decades. Despite technological advances, ultrasound images are suscepti...