AIMC Topic: Multimodal Imaging

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Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document.

European heart journal. Cardiovascular Imaging
In this EACVI clinical scientific update, we will explore the current use of multi-modality imaging in the diagnosis, risk stratification, and follow-up of patients with aortic stenosis, with a particular focus on recent developments and future direc...

Artificial Intelligence Assisted Multi-modal Photoacoustic-Ultrasound Imaging for Studying Renal Tissue Function and Hemodynamics.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Combined functional-anatomic imaging modalities, which integrate the benefits of visualizing gross anatomy along with the functional or metabolic information of tissue has revolutionized the world of medical imaging. However, such existing imaging mo...

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

Journal of cardiovascular medicine (Hagerstown, Md.)
The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac mag...

[Progress in cardiac imaging: from echocardiography to multimodality imaging].

Giornale italiano di cardiologia (2006)
In the last few decades, echocardiography has represented one of the technological fields with the fastest evolution and progress. As a non-invasive method at relative low cost, it is also suitable for the future to an increasingly integrated use in ...

[Multimodal imaging and evaluation in the age of artificial intelligence].

Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Multimodal imaging is able to image the retina in unprecedented detail, and the joint analysis (integration) of these data not only enables the securing of diagnoses, but also a more precise definition; however, humans encounter temporal and cognitiv...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...

Recent developments in pediatric retina.

Current opinion in ophthalmology
PURPOSE OF REVIEW: Pediatric retina is an exciting, but also challenging field, where patient age and cooperation can limit ease of diagnosis of a broad range of congenital and acquired diseases, inherited retinal degenerations are mostly untreatable...

[Artificial intelligence in hybrid imaging].

Der Radiologe
CLINICAL ISSUE: Hybrid imaging enables the precise visualization of cellular metabolism by combining anatomical and metabolic information. Advances in artificial intelligence (AI) offer new methods for processing and evaluating this data.

Artificial Intelligence in Cardiovascular Imaging.

Methodist DeBakey cardiovascular journal
The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potent...

Hybrid 11C-MET PET/MRI Combined With "Machine Learning" in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016.

Clinical nuclear medicine
PURPOSE: With the advent of the revised WHO classification from 2016, molecular features, including isocitrate dehydrogenase (IDH) mutation have become important in glioma subtyping. This pilot trial analyzed the potential for C-methionine (MET) PET/...