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Lab.Equipment has curated a new article from the Biotech industry for you. This one is published by Genetic Engineering and Biotechnology News (GEN):
A COVID-19 diagnosis is routinely made by a positive test for the presence of SARS-CoV-2. However, the current tests are fraught with challenges. Not only are there shortages of kits, but they take time to complete and carry a possibility of false-negative results. Researchers at Mount Sinai sought an alternative method for rapid and accurate diagnosis of patients with COVID-19. They are the first in the country to use artificial intelligence (AI) combined with imaging, and clinical data to analyze patients with COVID-19. In doing this, 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.
This study, titled “Artificial intelligence–enabled rapid diagnosis of patients with COVID-19,” was published recently in Nature Medicine and could help hospitals across the world quickly detect the virus, isolate patients, and prevent it from spreading during this pandemic.
“AI has huge potential for analyzing large amounts of data quickly, an attribute that can have a big impact in a situation such as a pandemic. At Mount Sinai, we recognized this early and were able to mobilize the expertise of our faculty and our international collaborations to work on implementing a novel AI model using CT data from COVID-19 patients in Chinese medical centers. We were able to show that the AI model was as accurate as an experienced radiologist in diagnosing the disease, and even better in some cases where there was no clear sign of lung disease on CT,” said one of the lead authors, Zahi Fayad, PhD, director of the BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai. “We’re now working on how to use this at home and share our fi... You can read the full article in the GEN – Genetic Engineering and Biotechnology News Blog.
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