Radiology

O-RADS for assessment of adnexal lesions: current status, challenges and future directions.

The Ovarian-Adnexal Reporting and Data System (O-RADS), developed by the American College of Radiology (ACR), provides a standardized, evidence-based framework for risk stratification and management of ovarian and adnexal lesions. The O-RADS system is comprised of two complementary imaging arms: ultrasound (US) and magnetic resonance imaging (MRI). O-RADS US serves as the primary imaging modality ...

A fully optimized deep learning framework for explainable and efficient kidney stone detection in computed tomography imaging.

PURPOSE: To develop and rigorously evaluate a hyperparameter-optimized custom convolutional neural network for kidney stone detection on axial computed tomography (CT) and to compare it, under matched optimization conditions, with widely used pretrained backbones, emphasizing statistical reliability, computational efficiency, and cross-institutional generalization. METHODS: A custom convolutional ...

A dual-contrast deep learning model for automated assessment of sarcopenia using MRI.

PURPOSE: To develop a dual-contrast deep-learning model for automated muscle segmentation on spin-echo and Dixon magnetic resonance imaging (MRI) for ...

Prediction of early recurrence in primary intussusception: development of an ultrasound-based radiomics and deep learning nomogram.

BACKGROUND: Early recurrence of intussusception frequently occurs within 48 h after reduction, yet current assessments lack objective tools for accura...

Advancing breast cancer diagnosis through ultrasound imaging: A cross-attention multi-scale vision transformer approach with comparative analysis against ResNet architectures.

In recent years, the field of medical imaging has witnessed substantial progress due to the integration of advanced machine learning techniques, parti...

The diagnostic and predictive value of AI-combined multilayer spiral CT for MACE after emergency PCI in STEMI patients: A prospective cohort study.

ST-segment elevation myocardial infarction (STEMI) patients remain at substantial risk for major adverse cardiovascular events (MACE) following emerge...

Large language models in radiology exams: A cross-sectional comparative analysis of performance in Turkish and English.

This study evaluated the success of large language models on radiology questions, analyzing language variations, temporal consistency, and performance...

Shared decision-making in radiology: leadership levers for patient-centred imaging.

BACKGROUND: Shared decision-making (SDM) is a cornerstone of patient-centred care, yet it has been underused in radiology. OBJECTIVE: To translate res...

Radiation scatter at fluoroscopy-guided endoscopic procedures: Building towards further optimization.

INTRODUCTION: Exposure to ionizing radiation by endoscopy personnel during fluoroscopy-guided procedures remains a health hazard. We aimed to evaluate...

Machine learning prediction of axillary lymph node metastasis using multimodal ultrasound in breast cancer.

OBJECTIVES: Accurate prediction of axillary lymph node metastasis (ALNM) is crucial for tailoring breast cancer treatments, this study aimed to develo...

Deep-Learning-Enhanced Bioimaging Via Energy Traps Regulated Lanthanide Nanoparticles.

High-resolution biological imaging in deep tissues holds substantial importance for advancing precision medicine. Currently, the lanthanide-doped nano...

Prostatic artery embolization and imaging-based biomarkers: current tools in monitoring treatment response and emerging strategies.

Prostatic artery embolization (PAE) is a safe and effective minimally invasive treatment for lower urinary tract symptoms (LUTS) attributed to benign ...

Multidrug resistance in cancer: current understandings and future perspective.

Multidrug resistance (MDR) remains the core clinical barrier limiting the achievement of durable and effective treatment for various tumors. With the ...

[The digital patient journey : Performing radiological examinations].

BACKGROUND: The rising demand for imaging studies, increasing diagnostic complexity, and limited personnel resources are organizational challenges for...

Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling.

BACKGROUND: Acute ischemic stroke is a time-critical neurological emergency in which imaging directly influences diagnosis, treatment eligibility, tis...

Design and analysis strategies for robust microbiome ageing research.

The gut microbiome changes systematically with age and associates with age-related morbidity and mortality, establishing it as a candidate biomarker a...

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In v...

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering.

Humans and other animals are driven to acquire information about opportunities in their environments, yet how they evaluate what is worth learning rem...

Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.

PURPOSE: This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve ...