Artificial Intelligence

Using less than 1,000 imaging cases, researchers from Massachusetts General Hospital (MGH) in Boston were able to train an artificial intelligence (AI) algorithm to detect intracranial hemorrhage (ICH) and classify its five subtypes on unenhanced head CT scans, according to research published in the journal Nature Biomedical Engineering.

Follow-up recommendations in radiology reports commonly contain little standardization. Machine learning and deep learning methods are each effective for deciphering reports and may provide the foundation for real-time recommendation extraction, according to a recent study in the Journal of the American College of Radiology.

Researchers out of the University of Surrey in the U.K. created an artificial intelligence (AI) platform that can predict which cancer patients are most at risk for experiencing common symptoms associated with the disease.

Radiomic features extracted from CT images accurately distinguished small-cell lung cancer from benign nodules, according to a retrospective study published Dec. 18 in Radiology.

The desire to deliver patient-centered care drives many caring and high-achieving individuals to pursue a career in medicine, and AI can unburden today's physicians so they can stay focused on that primary goal.

At the RSNA’s 2018 Annual Meeting in Chicago, Aidoc announced a new partnership with the American College of Radiology Science Institute to create standard solutions for the integration of AI into medical imaging and radiologists’ daily workflow.

Artificial intelligence (AI) can generate high-quality amyloid PET images from simultaneously acquired MR images and ultra-low-dose PET data, according to a Dec. 11 study published in Radiology.

“If radiologists are expected to utilize machine learning models safely and effectively for imaging interpretation, education for all levels of background and experience will be required, and a formalized machine learning curriculum targeted toward early career radiologists and trainees is urgently needed," Monica J. Wood, MD and colleagues wrote.

A deep learning algorithm developed by researchers at the Mayo Clinic in Rochester, Minnesota, segmented abdominal CT images to determine body composition similarly to, and at times, better than trained radiologists.

An AI software system called SmartTarget—which overlays tumor information from MRI scans onto ultrasound images—may help guide surgeons in conducting biopsies and improve prostate cancer detection by reducing the number of unidentified cases, according to research publishing Dec. 5 in the journal European Urology.

Global research and development firm Cambridge Consultants, in the U.K. has developed an artificial intelligence (AI) software able to reconstruct images in real-time that have been previously damaged or obscured, according to a Dec. 4 report by Venture Beat.

A deep learning-based approach accurately predicted aneurysm size from magnetic resonance angiography (MRA) images, reported the authors of a Dec. 3 study published in the Journal of Digital Imaging.