AIMC Topic: Image Interpretation, Computer-Assisted
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Artificial intelligence software in biomedical imaging: a pharmaceutical perspective on radiology and contrast-enhanced ultrasound applications.
Clinical and experimental rheumatology
May 1, 2025
Artificial intelligence (AI) is rapidly transforming radiology, with over 200 CE-marked products in the EU and more than 750 AI-based devices authorised by the FDA in the US, mainly used for x-ray, CT, MRI, and ultrasound imaging. Despite regulatory ...
AI-augmented reconstruction provides improved image quality and enables shorter breath-holds in contrast-enhanced liver MRI.
European radiology experimental
May 1, 2025
BACKGROUND: To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI).
Effects of Deep Learning-Based Reconstruction on the Quality of Accelerated Contrast-Enhanced Neck MRI.
Korean journal of radiology
May 1, 2025
OBJECTIVE: To compare the quality of deep learning-reconstructed turbo spin-echo (DL-TSE) and conventionally interpolated turbo spin-echo (Conv-TSE) techniques in contrast-enhanced MRI of the neck.
Artificial Intelligence in Spine Imaging: A Paradigm Shift in Diagnosis and Care.
Magnetic resonance imaging clinics of North America
May 1, 2025
Recent advancements in artificial intelligence (AI) can significantly improve radiologists' workflow, improving efficiency and diagnostic accuracy. Current AI applications within spine imaging are approved to accelerate image acquisition time, improv...
Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.
Radiology. Imaging cancer
May 1, 2025
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...
Deep Anatomical Federated Network (Dafne): An Open Client-Server Framework for Continuous, Collaborative Improvement of Deep Learning-based Medical Image Segmentation.
Radiology. Artificial intelligence
May 1, 2025
Purpose To present and evaluate Dafne (deep anatomical federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of radiologic images through federated incremental learning. Materials and M...
Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI.
Radiology. Artificial intelligence
May 1, 2025
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...
Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.
Radiology. Artificial intelligence
May 1, 2025
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...
Deep Learning-based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy.
Radiology. Artificial intelligence
May 1, 2025
Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials...
Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning-assisted Prostate Cancer Classification Using Multiparametric MRI.
Radiology. Artificial intelligence
May 1, 2025
Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, ...
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