Radiology

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

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Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study.

In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research...

Understanding ChatGPT for evidence-based utilization in interventional radiology.

Advancement in artificial intelligence (AI) has the potential to improve the efficiency and accuracy...

The Success of Deep Learning Modalities in Evaluating Modic Changes.

BACKGROUND: Modic changes are pathologies that are common in the population and cause low back pain....

Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model.

PURPOSE: To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomo...

Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis.

OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, w...

A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.

Urinary tract infection (UTI) is a common disorder. Its diagnosis can be made by microscopic examina...

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.

This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver a...

CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for ...

Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy.

INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion ...

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

Robust and reliable diagnostic methods are desired in various types of industries. This article pres...

Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy du...

Bidirectional Encoder Representations from Transformers in Radiology: A Systematic Review of Natural Language Processing Applications.

INTRODUCTION: Bidirectional Encoder Representations from Transformers (BERT), introduced in 2018, ha...

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.

We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative ...

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.

Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, p...

Artificial intelligence model GPT4 narrowly fails simulated radiological protection exam.

This study assesses the efficacy of Generative Pre-Trained Transformers (GPT) published by OpenAI in...

Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correctio...

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evalua...

Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images.

Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantificatio...

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individ...

Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis.

PURPOSE: Although several studies have compared the performance of deep learning (DL) models and rad...

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