New Study Finds that RADLogics AI-Powered Solution Achieves High Accuracy for Detecting COVID-19 on CT
RADLogics™ announced today new research that validates the performance of an AI-powered CT image analysis solution that is designed to automatically and accurately detect COVID-19 (Coronavirus) and quantify the disease burden in affected patients. “As the novel coronavirus continues to rapidly spread around the world, healthcare systems and providers may become overwhelmed with symptomatic patients that require testing, imaging, and treatment,” said RADLogics’ CEO and Co-Founder Moshe Becker. “In an effort to help alleviate this burden on the world’s healthcare providers – and to support improved patient outcomes – we dedicated our resources toward successfully modifying and adapting our existing AI models to develop this solution specifically for COVID-19 detection and quantification. To date, we have already deployed our solution in China, Russia and Italy, and we are rapidly scaling in other countries in response to the strong demand.” The study, led by Professor Hayit Greenspan from Tel Aviv University and RADLogics, in collaboration with medical experts from the US and China including Dr. Eliot Siegel of the University of Maryland School of Medicine in Baltimore, MD; and Dr. Adam Bernheim of the Icahn School of Medicine at Mount Sinai in New York, NY; found that the CT image analysis algorithm – developed from multiple international datasets – was able to differentiate 157 patients with and without COVID-19 with a 0.996 AUC (plus, 98.2 percent sensitivity and 92.2 percent specificity). A consistent and reproducible method for rapidly screening and evaluating high volumes of thoracic CT imaging studies can assist healthcare systems through this pandemic by augmenting radiologists and acute care teams that could be overwhelmed with patients.
Artificial Intelligence & Machine learning
Graphics / Imaging / Animation / GUI / Multimedia
Computer Aided Design (CAD)
Engineering & Scientific
Data Warehousing Software