Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.

PURPOSE: Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a ne...

Automated digital templating of component sizing is accurate in robotic total hip arthroplasty when compared to predicate software.

Accurate pre-operative templating of prosthesis components is an essential factor in successful tota...

Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

BACKGROUND: Attenuation and scatter correction is crucial for quantitative positron emission tomogra...

Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.

Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We ai...

Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics...

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

OBJECTIVES: To prospectively investigate whether fully automated artificial intelligence (FAAI)-base...

Diagnostic evaluation of deep learning accelerated lumbar spine MRI.

BACKGROUND AND PURPOSE: Deep learning (DL) accelerated MR techniques have emerged as a promising app...

Advances in the Application of Artificial Intelligence in Fetal Echocardiography.

Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in f...

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imagin...

Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.

This study focused on a novel strategy that combines deep learning and radiomics to predict epiderma...

Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality.

BACKGROUND AND PURPOSE: This study explores the use of deep learning (DL) techniques in MRI of the o...

Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.

Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound ima...

Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.

BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accur...

Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previou...

Automated Measurement of Ovary Development in Atlantic Salmon Using Deep Learning.

OBJECTIVE: Salmon breeding companies control the egg stripping period through environmental change, ...

Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images.

OBJECTIVES: Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected t...

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.

BACKGROUND: Accurate diagnosis and early treatment are essential in the fight against lymphatic canc...

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

BACKGROUND: Many screening and diagnostic methods are currently available for biliary atresia (BA), ...

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

INTRODUCTION: In the last few years, several models trying to calculate the biological brain age hav...

Artificial Intelligence Chatbots' Understanding of the Risks and Benefits of Computed Tomography and Magnetic Resonance Imaging Scenarios.

PURPOSE: Patients may seek online information to better understand medical imaging procedures. The p...

Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver.

AIM: To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional i...

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