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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Ron Blankstein, MD, Brigham and Womens Hospital, explains a study using AI opportunistic screening in non-cardiac CT scans looking for coronary artery disease.

Use of AI opportunistic screening in CT for cardiovascular disease

Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.

artificial intelligence in cardiology

FDA grants AI-powered ECG screening tool for aortic stenosis its breakthrough device designation

The FDA clearly sees significant potential in this new screening software from New York-based AccurKardia.

Left, coronary CT angiography of a vessel showing plaque heavy calcium burden. Right, image showing color code of various types of plaque morphology showing the complexity of these lesions. The right image was processed using the FDA cleared, AI-enabled plaque assessment from Elucid.

FDA clears new software for AI-powered CCTA assessments

Elucid's PlaqueIQ was trained to turn CCTA images into interactive 3D reports that help physicians visualize the presence of atherosclerosis.

Breast arterial calcifications (BACs) identified on screening mammograms may help identify women who face a heightened risk of developing cardiovascular disease (CVD), according to a new analysis published in Clinical Imaging.

Younger women with breast arterial calcifications are at markedly higher risk of major cardiovascular events

Currently, there is no standardized reporting requirement related to BACs, and ACR classifies reporting vascular calcifications on breast imaging as optional. 

voice audio recording smartphone

AI detects hypertension in voice recordings

A machine learning-powered smartphone app was trained to detect hundreds of biomarkers in the human voice, using even the tiniest detail to anticipate when patients may present with symptoms of hypertension. 

Harlan Krumholz, MD, SM, editor-in-chief of the Journal of the American College of Cardiology (JACC), and a cardiologist and the Harold H. Hines, Jr. Professor of Medicine at the Yale school Medicine, explains some of the key technology advances he is watching across cardiology.

JACC editor excited by progress during a 'very important moment' for cardiology

Harlan Krumholz, MD, editor-in-chief of the Journal of the American College of Cardiology, explains some of the key technology and treatment trends he has his eyes on.

The rapid rise of artificial intelligence (AI) has helped cardiologists, radiologists, nurses and other healthcare providers embrace precision medicine in a way that ensures more heart patients are receiving personalized care.

FDA clears AI platform for planning ahead of TAVR, other heart procedures

The cloud-based platform identifies and measures cardiac structures in CT scans.

robot reviewing heart data

Viz.ai partners with Cleerly in the name of AI-based CCTA evaluations

The new partnership is focused on getting advanced AI algorithms into the hands of cardiologists.

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Around the web

Radiology Business
Key takeaways from RSNA 2024 for AI, CT, MRI and the radiology staffing shortage

Several key trends were evident at the Radiological Society of North America 2024 meeting, including new CT and MR technology and evolving adoption of artificial intelligence.

Health Imaging
Use of AI opportunistic screening in CT for cardiovascular disease

Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.