This solution has been studied via multiple international datasets and a range of retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT images.
COVID-19, or coronavirus, continues to rapidly spread around the world, and healthcare systems and providers—including radiology teams—may soon become overwhelmed with the volume of patients who will require testing, imaging, and treatment for this disease.
As a healthcare artificial intelligence (AI) software company, RADLogics recognizes the power of AI image analysis for making radiology image interpretation faster and more effective. To alleviate the inevitable burden on health systems, the RADLogics has rapidly applied machine learning image analysis and interpretation to automatically detect, quantify, and track COVID-19 via thoracic CT exams.
RADLogics has rapidly developed its AI-based CT image analysis tools to automatically and accurately detect the COVID-19 / coronavirus in large numbers of CT studies. For patients with COVID-19, the RADLogics solution classifies results per thoracic CT studies utilizing deep-learning image analysis. Our AI-based image analysis further outputs a suggested “corona score” to measure the progression of patients’ disease and/or recovery over time.
RADLogics, founded in 2010, is a healthcare software company developing artificial intelligence (AI)-powered solutions, RADLogics provides machine learning image analysis solutions to improve radiologists’ productivity while enhancing patient outcomes. Based in Boston, MA, US, and Tel Aviv, Israel, RADLogics is one of the pioneers in using AI & machine learning image analysis and advanced big data analytics to search and analyze imaging data from CTs, MRIs, PET scans, and X-rays to help reduce diagnostics turnaround time from hours to minutes by automating detection and report generation functions. The company’s patented AI medical image analysis platform enables rapid development of AI algorithms, and provides seamless integration into existing radiology workflow.