Radiology

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

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Flexible large-area ultrasound arrays for medical applications made using embossed polymer structures.

With the huge progress in micro-electronics and artificial intelligence, the ultrasound probe has be...

Extended pelvic lymph node dissection in robot-assisted radical prostatectomy is an independent risk factor for major complications.

The aim of this study is to evaluate the major postoperative complication rate after robot-assisted ...

Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis.

BACKGROUND: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms an...

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forwa...

Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.

PURPOSE: To propose a five-point scale for radiology report importance called Report Importance Cate...

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically afforda...

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

BACKGROUND: Movement disorders such as Parkinson's disease are associated with structural and functi...

AI-assisted automatic MRI-based tongue volume evaluation in motor neuron disease (MND).

PURPOSE: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the...

A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images.

Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cos...

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-F...

Employing deep learning and transfer learning for accurate brain tumor detection.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with h...

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tool...

Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study.

AIM: To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnet...

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography.

PURPOSE: The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) mo...

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

OBJECTIVE: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammo...

Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future.

The utilization of artificial intelligence (AI) in medical imaging has become a rapidly growing fiel...

Machine learning-based analysis of Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes....

From pixels to prognosis: Imaging biomarkers for discrimination and outcome prediction of pulmonary embolism : Original Research Article.

PURPOSE: Recent advancements in medical imaging have transformed diagnostic assessments, offering ex...

A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation.

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in dis...

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