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Molecular Imaging

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Artificial Intelligence for Brain Molecular Imaging.

PET clinics
AI has been applied to brain molecular imaging for over 30 years. The past two decades, have seen explosive progress. AI applications span from operations processes such as attenuation correction and image generation, to disease diagnosis and predict...

Demystifying Medico-legal Challenges of Artificial Intelligence Applications in Molecular Imaging and Therapy.

PET clinics
Artificial Intelligence (AI) has been rapidly embraced by imaging fields and offers a variety of benefits in nuclear medicine; however, the biggest hurdles to AI in health care will likely not be technological but legal. What happens when an error oc...

Deep learning for in vivo near-infrared imaging.

Proceedings of the National Academy of Sciences of the United States of America
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 ...

Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging.

Current radiopharmaceuticals
In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applic...

Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

European journal of nuclear medicine and molecular imaging
Artificial intelligence involves a wide range of smart techniques that are applicable to medical services including nuclear medicine. Recent advances in computer power, availability of accumulated digital archives containing large amount of patient i...

Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery.

Nature reviews. Cardiology
Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries ...

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology.

Molecular imaging
Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artific...

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

PloS one
The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at le...

Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.

Methods in molecular biology (Clifton, N.J.)
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus...