RADLogics Announces New Advancements and Research in AI-Powered COVID-19 Medical Imaging Solutions
The company’s latest algorithm enhancements to help RADLogics distinguish between Coronavirus and other abnormalities such as common pneumonia on chest CT scans
RADLogics announced the availability of the latest version of the company’s AI-Powered COVID-19 CT algorithm. Building on the company’s AI-Powered solution that has processed and analyzed hundreds of thousands of suspected Coronavirus cases globally, the latest update delivers a complex deep learning system consisting of several models utilized to detect, localize and segment regions in the lungs infected with COVID-19. With Coronavirus cases again surging across the U.S. along with hospitalizations, AI-Powered solutions are poised to not only alleviate the increased burden associated with COVID-19, but to help support improved outcomes by reducing burnout and errors.
As part of RADLogics’ latest version of its algorithm, three different analyses can now be performed simultaneously on raw chest CT image scans including: 1) a lungs region-of-interest are cropped with lung abnormalities detected; 2) the lung lobes are segmented and; 3) if the nodules plug-in is activated, focal Ground Glass Opacities (GGOs) are detected. According to leading physicians, these measurements are key features in determining patient classification into COVID-19 and non-COVID-19 indications. The overall system produces the decision whether the case is suspected for COVID-19 with a confidence level (in percentages). These measurements along with other features are used by radiologists to distinguish between COVID-19 and other abnormalities such as common pneumonia.
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RADLogics AI-Powered COVID-19 solutions provide quantitative analysis to clinical teams that perform testing and measurement of patients with severe or worsening respiratory
“With the U.S. in the midst of an unprecedented rise in COVID-19 infections, with current hospitalizations at an all-time record of more than 90,000 patients, there is an increasing need for AI solutions in medical imaging,” said Moshe Becker, CEO and Co-Founder of RADLogics. “Coronavirus-related infection rates are experiencing a sharp increase in most states – from rural communities to urban areas – that have the potential to overwhelm ER, ICU and radiology teams with a surge of patients, and AI-Powered medical imaging analysis solutions are poised to reduce this pressure through improved patient triage, monitoring and management.”
Since the pandemic started earlier this year, RADLogics has responded with the deployment of the company’s AI-Powered medical image analysis solution worldwide. In accordance with FDA guidance for imaging systems and software to address the COVID-19 public health emergency, RADLogics has made its FDA cleared CT and X-ray solutions available to hospitals and healthcare systems throughout the U.S. for patient triage and management. Designed for easy installation and integration both on-site and via the cloud, RADLogics’ algorithms are supported by the company’s patented software platform that enables rapid deployment at multiple hospitals, and seamless integration with existing workflows. All of the company’s AI-Powered solutions are available worldwide through major OEM distribution partners including Nuance via the AI Marketplace in the U.S. market.
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To further validate the ability of AI to distinguish COVID-19 from other respiratory diseases, the company also highlighted a new research paper in preprint entitled “Automated triage of COVID-19 from various lung abnormalities using chest CT features”. Led by Professor Hayit Greenspan from Tel Aviv University and members of the RADLogics’ algorithm development team, the paper studied a fully automated AI-based system that takes as input chest CT scans and triages COVID-19 cases.
The study explored multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). The research evaluated the system on a dataset of 2,191 CT cases and demonstrated a robust outcome with 90.8 percent sensitivity at 85.4 percent specificity with 94.0 percent ROC-AUC. Results of this study are available on arXiv.org, and it has been submitted for review and potential publication.
RADLogics will be featured this week as part of the RSNA 2020 Imaging AI in Practice demonstration. In addition, RADLogics will be included in the Nuance virtual booth as part of the company’s AI Marketplace display.