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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Multiple sclerosis lesions detected with AI assistance

AI assistance helps rads shave 1/3 of their reporting times for MS lesions

Assessing multiple sclerosis is a time-consuming process, making reducing the burden an interest of multiple AI vendors.

HeartFlow introduced its next generation artificial intelligence (AI) Plaque Analysis with an interactive experience at SCCT 2024. It shows a 3D plaque model and analysis by territory across calcified, non-calcified and low-attenuation plaques. This includes viewing cross-sectional, color-coded images of each plaque type where it was quantified along the vessel. #SCCT #SCCT24 #SCCT2024

Updated HeartFlow technology offers interactive features, full integration with FFR-CT 

HeartFlow is using SCCT 2024 to introduce the world to its updated Plaque Analysis platform. “Accurately diagnosing a patient’s risk for coronary artery disease is critical for determining the best treatment," Chief Medical Officer Campbell Rogers, MD, explained.

test exam scantron

Developing board-style radiology questions is resource intensive. Large language models could help

Crafting these materials is typically left up to radiologists who draw from their own educational and clinical experiences, but the process is time consuming and can incur significant costs.

SCCT aims to provide common language for CCTA use with updated guidance

The Society of Cardiovascular Computed Tomography has developed a new expert consensus document designed to get physicians on the same page when discussing this growing technology. 

AI can accurately complete leg length and angular measurements

AI tool accurately completes leg length measurements up to 87% faster than human readers

Manual leg length measurements are time-consuming and can be challenging to reproduce.

Researchers have developed and validated a new artificial intelligence (AI) model capable of producing four-chamber cardiac MR (CMR) images in seconds, publishing their findings in European Radiology Experimental.

AI creates accurate 4D heart scans in seconds

New data out of England provides cardiologists and radiologists with another reminder of AI's potential to transform healthcare. 

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AI tool's supplemental MRI recs help detect 4 times more breast cancers than density measures

What’s more, many of the cancers detected were invasive or multifocal, making their detection more timely. 

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How does AI TI-RADS compare to other thyroid nodule risk stratification systems?

Experts are hopeful that the reporting and data system could simplify the risk stratification of thyroid nodules in the future. 

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Around the web

Cardiovascular Business
Global shortage of nuclear imaging isotopes may be over

The nuclear imaging isotope shortage of molybdenum-99 may be over now that the sidelined reactor is restarting. ASNC's president says PET and new SPECT technologies helped cardiac imaging labs better weather the storm.

Cardiovascular Business
‘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.

Cardiovascular Business
FDA clears AI tool that flags signs of heart disease in chest CT scans

The newly cleared offering, AutoChamber, was designed with opportunistic screening in mind. It can evaluate many different kinds of CT images, including those originally gathered to screen patients for lung cancer. 

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