CT quantifies COVID-19 severity, ongoing conditions By Kate Madden Yee, AuntMinnie.com staff writer
Throughout this year’s COVID-19 pandemic, chest CT has proven to be a valuable tool for diagnosing the illness in particular clinical situations -- such as when reverse transcription polymerase chain reaction (RT-PCR) testing isn’t readily available or results are delayed. But the modality has also shown value in assessing the severity of the disease and evaluating ongoing conditions that can plague recovered patients, especially when combined with artificial intelligence (AI), according to several presentations in a scientific session on chest imaging delivered Sunday at the RSNA 2020 meeting. Predicting severity In the afternoon session, a team led by Ziyue Xu, PhD, senior scientist at graphics processing unit technology developer Nvidia, shared results from a study the company conducted that combined a deep-learning algorithm with chest CT to predict whether COVID-19 patients would be admitted to the intensive care unit (ICU). The findings suggest that AI could boost CT’s performance when it comes to helping clinicians predict COVID-19 severity, Xu told session attendees. “[This deep-learning algorithm can] alert the clinician to the enhanced potential of ICU admission, when combined with other clinical features,” he said. For the study, Xu and colleagues included 632
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chest CT scans from patients with COVID-19 confirmed by RT-PCR testing; of these, 69 patients were admitted to the ICU and 563 were not. The team developed a whole-lung segmentation algorithm and assessed its effectiveness when used with CT by overall accuracy, sensitivity, and specificity. The algorithm achieved high accuracy, specificity, and negative predictive value (NPV) for identifying COVID-19 and predicting ICU admission on chest CT.
“Based upon chest CT alone, AI-based deeplearning algorithms can reasonably predict clinical outcomes such as ICU admission in patients with COVID-19 who underwent CT and PCR on the day of admission,” Xu concluded. “The model is feasible with reasonable accuracy and specificity of prediction.” Predicting prevalence In a second Sunday afternoon presentation, Italian researchers looked at whether CT’s performance varied depending on the prevalence of COVID-19 disease in a region. A group led by Dr. Marcello Petrini of Guglielmo da Saliceto Hospital in Piacenza, Italy, assessed the modality’s diagnostic performance for severe illness by comparing an outbreak phase to an ensuing period of lower disease incidence (first outbreak, high prevalence: February 21 to March 7; second period, lower prevalence after 28 days of lockdown: April 6-13).
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