A deep learning algorithm developed using imaging data from more than 1,000 Alzheimer’s disease (AD) patients can accurately predict the presence of AD more than six years before a doctor finalizes a diagnosis, researchers reported in Radiology Nov. 6.
On Oct. 26, the American College of Radiology Data Science Institute (ACR DSI) announced the release of standardized artificial intelligence (AI) use cases designed to improve AI adoption in radiology. Why, exactly, are these use cases so vital to the specialty?
The American College of Radiology (ACR) Data Science Institute (DSI) announced the release of its first series of freely available standardized artificial intelligence (AI) use cases to increase the utilization of AI adoption in medical imaging.
A deep learning algorithm can automatically detect lumbar vertebrae in MRI images, according to findings published in the Journal of Digital Imaging. This, the authors noted, has potential to improve clinician efficiency.
Specialists can improve aneurysm detection rates by using a deep learning algorithm that provides a second assessment of images already interpreted by radiologists, according to new findings published in Radiology.
Machine learning (ML) has become one of the hottest topics in radiology and all of healthcare, but reading the latest and greatest ML research can be difficult, even for experienced medical professionals. A new analysis published in the American Journal of Roentgenology was written with that very problem in mind.
A natural language processing (NLP) and machine learning algorithm was trained to evaluate variability in both free-text radiology reports and structured radiology reports, according to new research published in Current Problems in Diagnostic Radiology. The variation was more prevalent in free-text reports.
Gauss Surgical, a Los Altos, California-based healthcare technology company, announced that it raised $20 million in Series C funding to speed up the adoption of its Triton platform and develop new solutions.
The Massachusetts Institute of Technology (MIT) announced a $1 billion commitment to “address the global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence (AI).”
Researchers from Massachusetts General Hospital (MGH) and Harvard Medical School developed a deep learning (DL) model that measures breast density “at the level of an experienced mammographer.” Results of the study were published in Radiology.