AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Multimodal Imaging

Showing 231 to 240 of 248 articles

Clear Filters

[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/...

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Neuroinformatics
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET),...

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

IEEE transactions on medical imaging
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living ...

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.

IEEE transactions on medical imaging
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build th...