Radiology

A multimodal deep learning model for predicting severe hemorrhage in placenta previa.

Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using MRI T2-weighted image of the placenta and tabular data consisting of patient demographics and preoperative blood examination data, a multimodal deep le...

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). However, uncertainty in identification of radiologic UIP pattern leads to the continued need for invasive surgical biopsy. We thus developed and validate...

AI as a New Frontier in Contrast Media Research: Bridging the Gap Between Contrast Media Reduction, the Contrast-Free Question and New Application Discoveries.

Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-dr...

Unsupervised deep learning registration model for multimodal brain images.

Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unkn...

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual o...

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 concerns about dose exposure. New dose reduction s...

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 structured reporting of coronary artery disease accordi...

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 embolism and quantify its severity using the Qana...

Machine learning methods for tracer kinetic modelling.

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation i...

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 replacement (TAVR) using deep learning (DL) methods appl...

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

Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy and identification of the optimal view. Segmentatio...

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 detection in mammography, the quality of such lite...

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, specifically representing anatomical regions of interest ...

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

BACKGROUND: Compression ultrasonography of the leg is established for triaging proximal lower extremity deep vein thrombosis (DVT). AutoDVT, a machine...

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 (normal) or become cancerous, depending on the st...

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 clinical characteristics. The molecular subtypes of brea...

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 mammography imaging findings and predict the existence of ex...

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 is safer, faster, and more widely applicable. Howe...

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 radiotherapy, as it minimizes the potential adverse e...

Quantitative noninvasive measurement of cerebrospinal fluid flow in shunted hydrocephalus.

OBJECTIVE: Standard MRI protocols lack a quantitative sequence that can be used to evaluate shunt-treated patients with a history of hydrocephalus. Th...