Latest AI and machine learning research in radiology for healthcare professionals.
Background. Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) methods have emerged as a potential approach for synthesizing postcontrast T1-weighted images from precontrast sequences alone. Objective. The objective of this study was to systematically review DL-based synthesis of postcontrast T1-weighted MRI, characterize model architectures and evaluatio...
INTRODUCTION: Artificial intelligence (AI) is reshaping diagnostic paradigms across oncology. In ophthalmic oncology encompassing conditions like retinoblastoma and uveal melanoma, AI has immense potential due to the specialty's reliance on advanced imaging and the importance of early and accurate diagnosis. AREAS COVERED: This review explores recent developments in AI applications for ophthalmic ...
INTRODUCTION: This study aimed to identify optical coherence tomography (OCT) biomarkers at baseline and after the loading phase (LP) of antivascular ...
OBJECTIVES: To develop a deep learning-based multimodal framework for automated segmentation of orbital soft tissues and identify quantitative imaging...
BACKGROUND: Deep learning (DL) algorithms for digital breast tomosynthesis (DBT) have proliferated, demonstrating emerging potential in enhancing lesi...
OBJECTIVE: To enhance the diagnostic utility of 4D flow MRI in assessing cerebrospinal fluid (CSF) dynamics by super-resolving and denoising measured ...
This invited commentary grew out of a presentation made at the 2025 ConRad Meeting in Munich, Germany, and summarizes talks made by researchers suppor...
Transfusion-dependent β-thalassemia (B-TM) is complicated by progressive iron overload, remaining a primary cause of organ toxicity and mortality desp...
Positron emission tomography (PET) has been used in pediatric oncology since the modality gained traction 20 years ago but has been used more sparingl...
OBJECTIVES: To investigate mammographic features associated with high artificial intelligence (AI) risk scores as provided by two AI models applied to...
BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) is regarded as the non-invasive gold standard for myocardial ti...
A prospective observational cohort study. To determine whether machine learning models using radiomic features derived from preoperative MRI, clinical...
Accurate disease prognosis is essential for patient care but is often hindered by the scarcity of longitudinal data. This study explores deep learning...
INTRODUCTION: Currently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (...
PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide. Optical coherence tomography (OCT) and OCT angiography (OCTA) pr...
Yawning is a phylogenetically preserved and highly stereotyped behavior observed across vertebrates.1 In humans, it emerges early in development, as i...
BACKGROUND: Pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is a key prog...
RATIONALE AND OBJECTIVES: This study aimed to develop an interpretable machine learning (ML) model using diuretic ultrasonography to predict the neces...
People with semantic dementia (SD) or semantic variant primary progressive aphasia typically present with marked atrophy of the anterior temporal lobe...
Focused ultrasound (FUS) is an emerging therapeutic and diagnostic technology in neuro-oncology, offering new strategies for molecular diagnosis, drug...