Indica Labs, a provider of computational pathology software, and Octo, an information technology systems provider to the U.S. Federal Government, announced the online COVID Digital Pathology Repository (COVID-DPR), a virtual collection of high resolution microscopic COVID-related human tissue images hosted at the National Institutes of Health.  This repository will enable international collaboration by providing a centralized, cloud-based repository for sharing and annotating digital whole slide images of lung, liver, kidney and heart tissues from patients infected with COVID19, as well as the closely related coronaviruses associated with SARs and MERs. The whole slide images, annotations and metadata in the repository will be used as a reference data set for education, research and future clinical trials aimed at limiting further infection, disease, and death.  COVID-DPR is underpinned by Indica Labs' HALO LinkTM software, a collaborative image management platform designed specifically for secure sharing of digital whole slide images and data.  The current initiative involves multiple institutes within NIH, and COVID-DPR will be available immediately as a shared resource for researchers at institutes around the world with initial data sets being provided by infectious disease labs across North America, Europe, and Australia.

Mount Sinai researchers are the first in the country to use artificial intelligence (AI) combined with imaging, and clinical data to analyze patients with coronavirus disease (COVID-19). They have developed a unique algorithm that can rapidly detect COVID-19 based on how lung disease looks in computed tomography (CT scans) of the chest, in combination with patient information including symptoms, age, bloodwork, and possible contact with someone infected with the virus. Their study was published in the May 19 issue of Nature Medicine, and could help hospitals across the world quickly detect the virus, isolate patients, and prevent it from spreading during this pandemic.  The algorithm was shown to have statistically significantly higher sensitivity (84 percent) compared to 75 percent for radiologists evaluating the images and clinical data. The AI system also improved the detection of COVID-19-positive patients who had negative CT scans. Specifically, it recognized 68 percent of COVID-19-positive cases, whereas radiologists interpreted all of these cases as negative due to the negative CT appearance.  Mount Sinai researchers are now focused on further developing the model to find clues about how well patients will do based on subtleties in their CT data and clinical information. They say this could be important to optimize treatment and improve outcomes.

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