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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AI measures breast density as accurately as experienced mammographers

Breast imagers and artificial intelligence (AI) experts have shown that a new AI algorithm measures breast density with accuracy comparable to an experienced breast imager, according to new research published online Oct. 16 in Radiology.

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Mixed-reality headset enhances accuracy of external ventricular drain insertion

Using a mixed-reality holographic computer headset, neurosurgeons can more accurately perform external ventricular drain (EVD) insertion, according a new study out of Beijing published in the Journal of Neurosurgery.

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UCLA’s William Hsu named deputy editor of RSNA's new AI journal

William Hsu, PhD, a biomedical informatician and associate professor of radiology at UCLA, has been named deputy editor for the Radiological Society of North America (RSNA)’s new journal, Radiology: Artificial Intelligence.

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AI improves with input from radiologists

Artificial intelligence (AI) models utilizing radiologist-provided BI-RADS classification outperformed methods that did not use them, according to an Oct. 15 study in the Journal of the American College of Radiology.

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Microscopic imaging of kidney damage from contrast dyes opens door for novel drug therapies

Using microscopic imaging, researchers from the University of Calgary in Alberta, Canada have shown how the kidneys negatively respond to contrast dyes used during various medical tests and procedures, according to a university press release published Oct. 15.

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Novel AI approach can help radiologists improve osteoarthritis x-ray diagnosis

A novel convolutional neural network (CNN) approach could be used to help radiologists improve their classification of osteoarthritis (OA) on knee radiographs, reported authors of a new study published in the Journal of Digital Imaging.

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AI accurately distinguishes between false-positive, malignant, negative mammograms

Deep learning may accurately identify false-positive mammograms and distinguish such from images identified as malignant or negative, according to new research published Oct. 11 in the journal Clinical Cancer Research.

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AI can help non-radiologists diagnose pediatric elbow fractures

A team of California researchers used a deep convolutional neural network (DCNN) to accurately diagnose traumatic pediatric elbow joint diffusion, according to an Oct. 9 study published in the American Journal of Roentgenology.

Around the web

RBMA President Peter Moffatt discusses declining reimbursement rates, recruiting challenges and the role of artificial intelligence in transforming the industry.

Deepak Bhatt, MD, director of the Mount Sinai Fuster Heart Hospital and principal investigator of the TRANSFORM trial, explains an emerging technique for cardiac screening: combining coronary CT angiography with artificial intelligence for plaque analysis to create an approach similar to mammography.

A total of 16 cardiology practices from 12 states settled with the DOJ to resolve allegations they overbilled Medicare for imaging agents used to diagnose cardiovascular disease.