Radiology

Diagnostic evaluation of deep learning accelerated lumbar spine MRI.

BACKGROUND AND PURPOSE: Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image qu...

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 fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fe...

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonanc...

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 imaging are being proposed and developed. However, the p...

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 epidermal growth factor receptor (EGFR) mutations in patie...

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 images, posing a major challenge in the practical app...

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 orbit to enhance imaging. Standard protocols, altho...

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

BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic sple...

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 previously unfeasible, but ensuring quality and consisten...

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

OBJECTIVE: Salmon breeding companies control the egg stripping period through environmental change, which triggers the need to identify the state of m...

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 to have better reproducibility than manual delineat...

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 have been proposed based on structural magnetic reson...

Neuro-environmental interactions: a time sensitive matter.

INTRODUCTION: The assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environmen...

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), but the early and accurate diagnosis of BA remains...

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 purpose of this study was to assess the accuracy of...

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

RATIONALE AND OBJECTIVES: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The...

A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists.

RATIONALE AND OBJECTIVES: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myomet...

MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images.

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially ...

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management...

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.

RATIONALE AND OBJECTIVES: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) ...