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 could predict risk of Alzheimer’s on MRI 5 years before symptoms emerge

A new artificial intelligence (AI) algorithm developed by Canadian researchers can detect evidence of cognitive decline in brain MRI scans, genetics and clinical data, and may predict whether findings will lead to Alzheimer’s disease five years before symptoms appear.

Brain scans, AI evaluate surgeons’ response to stress, learning new skills

Determining how highly specialized surgeons and physicians learn new skills or respond to a stressful situation may be unveiled with recently published neuroimaging research, the Wall Street Journal reported on Oct. 3.

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Researchers use AI, brain MRI to predict learning difficulties in children

In a recent institutional study, artificial intelligence (AI) was found to identify learning difficulties in children struggling in school not previously detected or that did not match an existing diagnosis—such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder or dyslexia.

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Facebook to double size of AI research staff by 2020, increase medical imaging efforts

Facebook is planning to double the size of its Facebook Artificial Intelligence Research (FAIR) division by the year 2020 and grow to have 400 employees, according to a recent report by Forbes.

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Digital pathology algorithm accurately classifies 3 common dermatology diagnoses

A team of U.S. researchers accurately trained a deep learning convolutional neural network (CNN) to classify three common dermatopathology diagnoses, according to recent research published in the Journal of Pathology Informatics.

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3D printed phantom head may improve MRI safety before human testing

“We wanted to develop an anthropomorphic phantom head to help us better understand these issues by providing a safer way to test the imaging. We use the device to analyze, evaluate and calibrate the MRI systems and instrumentation before testing new protocols on human subjects,” Tamer Ibrahim, PhD, and co-creator said.

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Deep-learning platform detects malignant nodules on x-ray, beating radiologists

A deep learning-based automatic detection algorithm (DLAD) detected malignant pulmonary nodules on chest x-rays better than radiologists, according to a Sept. 25 study in Radiology.

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iPhone medical ultrasound device startup raises $250 million

The Butterfly Network, makers of the innovative iPhone ultrasound-on-chip named Butterfly iQ, announced it has raised $250 million to start shipping to 40 million healthcare practitioners globally, according to a report published Sept. 26 by Business Insider.

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.