Mini Review
Volume 2 Issue 10 - 2020
Review: Augmented Interpretable Intelligence in the Diagnostic Evaluation of COVID-19
Narayan A Viswanadhan1*, Andrew Borkowski2, Lauren A Deland2, Stephen M Mastorides2, Anmol Srivastava3 and Ayushman Sharma4
1Radiology Service, James A Haley VA Hospital, Tampa, Florida, USA
2Pathology and Laboratory Medicine Service, James A Haley VA Hospital, Tampa, Florida, USA
3Department of Computer Science, Northeastern University, Boston, Massachusetts, USA
4Department of Radiology, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
*Corresponding Author: Narayan A Viswanadhan, Radiology Service, James A Haley VA Hospital, Tampa, Florida, USA.
Received: August 24, 2020; Published: September 10, 2020




Abstract

COVID-19, caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. It has had a dramatic impact on society and world economies in only a few months. COVID-19 presents numerous challenges to all aspects of healthcare, including reliable methods for diagnosis, treatment, and prevention. In a time of resource scarcity, the ability to accurately and quickly sort affected from non-affected patients and to sort the infected into risk-adjusted groups during triaging, a vital task for maintaining health resources for those most in need of them, is of the utmost importance.

Artificial intelligence (AI) represents a potential solution to this problem. Machine learning (ML) is a subset of AI that employs deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than humans. We describe a number of various organ systems affected by COVID-19, and the potential applicability of using AI to aid in the interpretation and augment diagnosis of COVID-19 in the context of radiological examinations. In this review, we discuss a myriad of pulmonary, neurologic, and gastrointestinal manifestations of COVID-19, and examine potential applicability of artificial intelligence as it relates to image interpretation of these organ systems.

Keywords: COVID-19; Coronavirus; AI; Diagnosis; Radiology; Deep Learning

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Citation: Narayan A Viswanadhan., et al. “Review: Augmented Interpretable Intelligence in the Diagnostic Evaluation of COVID-19”. EC Nursing and Healthcare 2.10(2020): 09-13.

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