Radiology

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

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Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

PURPOSE: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on...

Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.

PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting an...

Deep learning corrects artifacts in RASER MRI profiles.

A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by ...

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

BACKGROUND: Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear ce...

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.

PURPOSE: Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. ...

Robust brain MRI image classification with SIBOW-SVM.

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting ...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffus...

Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.

BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), h...

Large language models for structured reporting in radiology: past, present, and future.

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality o...

Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome.

RATIONALE AND OBJECTIVES: To investigate the predictive value of a deep learning model based on mult...

Automated detection of motion artifacts in brain MR images using deep learning.

Quality assessment, including inspecting the images for artifacts, is a critical step during magneti...

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify ...

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enha...

Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.

Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of dat...

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagno...

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based mark...

AI in radiology: From promise to practice - A guide to effective integration.

While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology,...

Machine Learning for Localization of Premature Ventricular Contraction Origins: A Review.

Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ecto...

Machine learning models of cerebral oxygenation (rcSO) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The present study was designed to test the potential utility of regional cerebral oxygen saturation ...

MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.

OBJECTIVES: Despite showing encouraging outcomes, the precision of deep learning (DL) models using d...

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