Researchers at Concordia have developed an AI-assisted technique and a robotic platform that may one day help surgeons perform safer, faster and less invasive procedures to treat conditions such as blood clots located deep inside a patient’s neurovascular pathways.
Researchers at Washington University School of Medicine in St. Louis have developed an AI-based classifier that distinguishes between four common brain diseases that cause dementia: Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia and dementia with Lewy bodies, as well as healthy brain aging.
Coredio’s AI software is used to monitor HF parameters via wearables and standard blood pressure cuffs.
Kelley and his colleagues from the University of Rochester, Brown University, and the University of Copenhagen turned to artificial intelligence for help.
Spectral AI announced that the U.S. Food and Drug Administration (FDA) has granted De Novo classification for its DeepView System, authorizing the company to commence commercial distribution activities in the United States.
California-based Subtle Medical has announced FDA clearance for SubtleHD(PET), its next-generation PET image enhancement solution designed to improve image quality and accelerate PET imaging workflows across PET/CT and PET/MR systems.
Chicago, Illinois-based Cardiosense announced that the US Food and Drug Administration (FDA) has granted De Novo classification for its PCWP Analysis Software, which is designed to estimate noninvasively pulmonary capillary wedge pressure (PCWP), a key indicator of heart health.
Researchers from the National University of Singapore (NUS) have developed an AI-guided workflow that combines artificial intelligence (AI) with molecular simulations to identify potential drug candidates for diabetic wound healing, identifying folic acid, a common vitamin, as a top candidate.
A team of researchers from Carnegie Mellon University, in collaboration with Cleveland Clinic’s Cardiovascular Innovation Research Center, has developed an artificial intelligence (AI) system capable of interpreting some of the most complex heart scans in medicine, cardiac magnetic resonance imaging (MRI), without the need for manually labeled training data.
The study, conducted by researchers at Baylor College of Medicine and Texas Children’s Hospital, appeared in the American Journal of Human Genetics.