Radiology

Exploratory comparative analysis of automated morphologic and perfusion features derived from one-stop computed tomography angiography and computed tomography perfusion in large unruptured intracranial aneurysms.

PURPOSE: To compare rupture-related signals captured by automatically extracted computed tomography angiography (CTA) morphologic features and threshold-based computed tomography perfusion (CTP) metrics in large unruptured intracranial aneurysms (UIAs), and to explore a parsimonious feature combination for rupture status. METHODS: This retrospective cohort included 60 patients with UIAs who underw...

Artificial Intelligence-Assisted Wireless Handheld Ultrasound for Screening Developmental Dysplasia of the Hip in Infants.

OBJECTIVES: Ultrasonography is increasingly the preferred method for infant hip screening to enable timely diagnosis and treatment of developmental dysplasia of the hip (DDH). However, its reliance on experienced specialists and bulky equipment limits its application in routine screening, particularly in resource-limited and remote settings. We aimed to develop an artificial intelligence wireless ...

Development and validation of the SYSU-score for MRI-based transmural healing assessment in Crohn's disease: a dual-center study.

OBJECTIVES: Standardizing magnetic resonance enterography (MRE)-defined transmural healing (TH) remains challenging in Crohn's disease (CD) despite it...

GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.

OBJECTIVES: To evaluate whether GPT-4.1 and Llama 3.3 70B, large language models (LLMs) assessed in zero-shot, baseline configurations, detect and cat...

Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis.

BACKGROUND: Axillary lymph node metastasis (ALNM) is a critical prognostic factor in breast cancer. While sentinel lymph node biopsy remains the gold ...

Standardizing PSMA PET/CT reporting in prostate cancer: from structured frameworks to AI-assisted interpretation.

PSMA PET/CT is increasingly used for prostate cancer staging, restaging, treatment selection, and therapy response assessment. In parallel, several in...

Automatic metabolic breast cancer staging using [¹⁸F]FDG PET/CT: comparison with nuclear medicine physician-based and clinical staging.

PURPOSE: This study aimed to evaluate a deep-learning (DL)-based framework to automatically perform breast cancer (BC) metabolic staging on [¹⁸F]FDG P...

RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.

Explainable Artificial Intelligence (XAI) is gaining popularity in early diagnosis and monitoring of dementia. Herein, we recommend the incorporation ...

Establishing discard criteria for lead aprons using deep learning-based quantification of defect area on X-ray fluoroscopic video.

To establish an objective discard framework for lead aprons, defect areas quantified from X-ray fluoroscopy videos were linked to their corresponding ...

Methodological quality of cardiac CT and MRI radiomics studies assessed using METRICS and RQS by human readers and ChatGPT 5.1 Thinking.

OBJECTIVE: To assess the methodological quality of cardiac CT and MRI radiomics studies using the METhodological RadiomICs Score (METRICS) and Radiomi...

Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.

Artificial intelligence (AI) is reshaping healthcare, and radiology is at the forefront for adoption. Increasing demand for imaging, complex protocols...

Differentiating Ischemic From Nonischemic T-Wave Inversion Using a Multimodal Vision-Language Model With Reinforcement Learning (ECG-R1): Development and Validation Study.

BACKGROUND: The differentiation of primary ischemic from secondary nonischemic T-wave inversion (TWI) on electrocardiograms (ECGs) presents a critical...

Synchronous wearable ultrasound for early detection of coronary and carotid artery comorbidity.

Coronary heart disease (CHD) and carotid artery disease (CAD) often co-occur. However, conventional diagnosis typically involves separate, site-by-sit...

From diagnostic labels to radiology reports: a unified multi-modal framework for lesion detection and segmentation.

Deep learning-based vision models are playing an increasingly pivotal role in clinical diagnosis and treatment.However, existing approaches predominan...

Retinal Microvascular Signatures as Early Predictors of Cardiovascular Risk: Integrating Pathophysiology, Molecular Insights, and Artificial Intelligence.

Cardiovascular diseases (CVDs) continue to be a significant public health burden and public health emergency in the world, underscoring the importance...

Local performance and fairness testing of an AI Scribe in a paediatric developmental assessment clinic in South Australia: a silent trial protocol.

INTRODUCTION: Any tool that can reduce the administrative burden on healthcare providers while preserving safe, accountable and high-quality medical d...

Combination of color-Doppler ultrasound and MRI to improve lacrimal gland lesion characterization: A machine learning approach.

PURPOSE: The purpose of this study was to develop a machine learning-based algorithm based on a combination of magnetic resonance imaging (MRI) and co...

Mechanochemical disassembly pathways of self-assembled polymer-decorated PdnL2n supramolecular architectures.

Supramolecular PdnL2n architectures are versatile molecular platforms with applications spanning catalysis, sensing, and therapeutic delivery. Whereas...

HybridViT for robust wheat leaf disease detection using CLAHE and attention-based feature fusion.

Wheat (Triticum aestivum L.) is a staple crop of paramount importance to global food security; however, its productivity is significantly compromised ...

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Laryngeal...