Radiology

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

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Decreased liver-to-spleen ratio in low-dose computed tomography as a biomarker of fatty liver disease reflects risk for myocardial ischaemia.

AIMS: A strong association between fatty liver disease (FLD) and coronary artery disease is consiste...

Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI.

The incorporation of artificial intelligence into radiological clinical workflow is on the verge of ...

Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.

In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Lea...

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. Howe...

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique t...

Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies.

The application of artificial intelligence (AI) in the field of medicine has revolutionised various ...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regula...

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA.

Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of...

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction.

BACKGROUND: Proper maintenance of electro-medical devices is crucial for the quality of care to pati...

Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities.

OBJECTIVE: With the increasing amount and growing variety of healthcare data, multimodal machine lea...

Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the...

Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine.

Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic ...

Breast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis.

Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a crit...

The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose.

PURPOSE: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT lo...

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascula...

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interve...

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