AI Medical Compendium Journal:
European radiology

Showing 21 to 30 of 621 articles

SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

European radiology
OBJECTIVES: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo ...

Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.

European radiology
OBJECTIVES: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.

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

European radiology
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adopti...

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

European radiology
OBJECTIVES: Despite showing encouraging outcomes, the precision of deep learning (DL) models using different convolutional neural networks (CNNs) for diagnosis remains under investigation. This systematic review aims to summarise the status of DL MRI...

Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

European radiology
OBJECTIVES: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).

Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.

European radiology
OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS...

AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

European radiology
OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axia...

Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.

European radiology
OBJECTIVE: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) proto...

Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.

European radiology
OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or ima...