Artificial Intelligence

The study will determine whether CMAs can obtain echocardiograms that, when reviewed by cardiologists, will detect more patients with cardiac disease compared to a standard physical examination with an electrocardiogram (ECG) in a primary care setting, according to a Northwestern University press release.

A decision support tool can help physicians better diagnose bladder cancer treatment response on CT, according to an Nov. 10 study published in Academic Radiology.

Deep learning can estimate full-dose PET images from scans with significantly lower dosages, according to a new study in the Journal of Digital Imaging. The method may make performing PET scans safer and more affordable.

The FDA recently administered 510(k) clearance to software developed by MaxQ AI that uses AI to detect brain bleeds on CT images, according to a report published Nov. 8 by AI in Healthcare.

A team at the University of Queensland in Australia is working on a digital pathology slide technique that would speed up patient diagnosis and help address the country’s pathologist shortage, according to a university release.

Researchers created and validated a deep learning algorithm to improve the detection of polyps during colonoscopy, according to a study published in the latest issue of Nature Biomedical Engineering.

Researchers at the Icahn School of Medicine at Mount Sinai in New York City found convolutional neural networks (CNNs) trained to detect pneumonia on chest x-rays performed poorly when tested on data from outside health systems, according to a study published online Nov. 6 in PLOS Medicine.

Imperial College London announced its plans to open the London Medical Imaging and Artificial Intelligence Center for Value-Based Healthcare thanks to £10 million ($13 million) in recently awarded funds, according to a university news release published Nov. 6.

A new artificial intelligence (AI) algorithm trained on 18-F-fluorodeoxyglucose PET (18FDG-PET) scans can predict the occurrence of Alzheimer’s disease at least six years before diagnosis with 100 percent sensitivity, according to research published Nov. 6 in Radiology.

“This system has made everything much more transparent in that we can see everyone in every area of the hospital and it makes every area much more transparent because we can see what’s happening,” interventional radiologist Ellen Francesconi, MD, told HealthImaging.

Using a breast MRI tumor dataset, researchers found a deep learning convolutional neural network (CNN) approach could be trained to predict responses to chemotherapy prior to its initiation, according to a recent Journal of Digital Imaging study.

The American College of Radiology Data Science Institute (ACR DSI) recently released a series of standardized artificial intelligence (AI) use cases to help advance imaging in AI. Down the road, they could help create an “AI ecosystem” for radiology, wrote Bibb Allen, MD, chief medical officer of the ACR DSI, in a recent Journal of the American College of Radiology editorial.