Radiology

Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application.

OBJECTIVE: The increasing number of coronary computed tomography angiography (CCTA) requests raised ...

Revolutionising paediatric radiology: the future impact of artificial intelligence.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

PURPOSE: The purpose of this study was to propose a deep learning-based approach to detect pulmonary...

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for struc...

Machine learning methods for tracer kinetic modelling.

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantita...

Deep Learning to Estimate Left Ventricular Ejection Fraction From Routine Coronary Angiographic Images.

BACKGROUND: Cine images during coronary angiography contain a wealth of information besides the asse...

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement.

PURPOSE: To investigate survival prediction in patients undergoing transcatheter aortic valve replac...

Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches.

Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy a...

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.

 Although abundant literature is currently available on the use of deep learning for breast cancer ...

Remote Expert DVT Triaging of Novice-User Compression Sonography with AI-Guidance.

BACKGROUND: Compression ultrasonography of the leg is established for triaging proximal lower extrem...

A preliminary exploration into top-down and bottom-up deep-learning approaches to localising neuro-interventional point targets in volumetric MRI.

PURPOSE: Point localisation is a critical aspect of many interventional planning procedures, specifi...

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathe...

Multimodal hybrid convolutional neural network based brain tumor grade classification.

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy...

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinic...

Deep Learning-Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates.

Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve duri...

Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.

OBJECTIVES: To develop a multitask deep learning (DL) algorithm to automatically classify mammograph...

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging ...

Artificial Intelligence-Driven Image Quality Selection During Myocardial Contrast Echocardiography: A New Path to Precision.

The Current State of Artificial Intelligence and Its Intersection With Radiology.

A statistical deformation model-based data augmentation method for volumetric medical image segmentation.

The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning during rad...

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional ...