Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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Augmented datasets can improve accuracy of neural networks

Deep convolutional neural networks (DCNNs) can better classify chest x-rays when trained on augmented datasets, according to a new study published in Clinical Radiology.

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Ultrafast CT can produce clearer images in the ED

Utilizing the ultrafast scan mode for CT imaging in the emergency department (ED) can significantly reduce motion artifacts, reported a team of Japan-based researchers in a study published by the American Journal of Roentgenology.

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ACR, SIIM announce machine learning challenge for detecting pneumothorax

The Machine Learning Challenge on Pneumothorax Detection and Localization will kick-off at the SIIM 2019 Annual Meeting starting June 26 in Aurora, Colorado.

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Diffusion-tensor brain MRI of newborns helps predict neurological progression

“With information obtained from this study, it is possible that neuroimaging in newborns may to some extent predict neurodevelopment even for healthy children, and prenatal intervention targeted at improving white matter integrity at birth will be important for further promoting neurodevelopment in children,” wrote researchers of a new Radiology study.

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Brain imaging’s important role in understanding suicide

Aaron Williams was 16 years old when he committed suicide on the campus of his Charleston, South Carolina, high school in 2010. It was only until after the tragedy that neuroimaging revealed multiple lesions in his brain.

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AI can dramatically reduce mammography reads for radiologists

Machine learning can reduce a radiologists workload by lowering the number of screening mammograms they’re required to read while preserving accuracy, according to results of a feasibility study published in the Journal of the American College of Radiology.

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Deep learning with SPECT MPI can help diagnose heart disease

Deep learning designed to read single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) can improve the diagnosis of coronary artery disease—a killer of more than 370,000 people in the U.S. annually.

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Second research roadmap details priorities for AI in radiology

The report, put out by the Journal of the American College of Radiology, is a companion roadmap to part one which was published April 16 in Radiology.

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Positron partners with Upbeat Cardiology Solutions to improve cardiac PET/CT access

Positron, a New York-based nuclear imaging company, will now provide Upbeat Cardiology Solutions with advanced PET/CT systems and services. 

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‘A huge win’: CMS significantly increases Medicare payments for cardiac CT

CMS has more than doubled the CCTA payment rate from $175 to $357.13. The move, expected to have a significant impact on the utilization of cardiac CT, received immediate praise from imaging specialists.