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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China, Israel investing big in the medical imaging AI market

Reenita Das, a partner and senior vice president of healthcare and life sciences at Frost & Sullivan, wrote a new column examining the efforts being made by China and Israel to become leaders in the medical imaging artificial intelligence (AI) market.

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High-resolution image may be key to treating Zika virus

Researchers have captured the highest-resolution image to date of the Zika virus, which caused a global health crisis in 2015, and left thousands of babies with serious birth defects. The research team believes the finding may aid in designing a vaccine to fight the virus.

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8 key clinical applications of machine learning in radiology

Artificial intelligence (AI) and machine learning often get lumped together, but as the authors of a new Radiology commentary explained, the two terms are far from interchangeable. While machine learning is a specific field of data science that gives computers the ability to “learn” without being programmed with specific rules, AI is a more comprehensive term used to describe computers performing intelligent functions such as problem solving, planning, language processing and, yes, “learning.”

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FDA-approved AI echocardiogram software bests cardiologists in reducing LVEF variability

A deep-learning software that can automatically calculate left ventricular ejection fraction (LVEF) with less variability than a cardiologist recently received approval from the U.S. Food and Drug Administration (FDA).

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Deep learning software reduces variability in cardiovascular imaging

San Francisco-based tech company Bay Labs this week announced the success of its deep learning software, EchoMD AutoEF, in reducing variability in cardiovascular imaging.

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How radiologists can further AI training, help shape imaging’s future

As artificial intelligence (AI) and medical imaging continue to transform clinical practice, radiologists-in-training can no longer take a passive role in the march toward this coming change.

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Machine learning tool expedites detection of white matter lesions in stroke patients

A machine learning tool developed by researchers at Imperial College London could assess the severity of leukoaraiosis in stroke patients with greater efficiency and accuracy than the typical emergency room CT, a study published this week in Radiology states.

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Inexpensive eye imaging method may help monitor Alzheimer’s progression

A new study from Queen’s University Belfast researchers found the eye may be a critical indicator for Alzheimer’s disease (AD) along with a host of other neurodegenerative diseases.