Radiographic findings in COVID-19: Comparison between AI and radiologist

Context: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. Aim: This study evaluates and co...

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Main Authors: Arsh Sukhija, Mangal Mahajan, Priscilla C Joshi, John Dsouza, Nagesh DN Seth, Karamchand H Patil
Format: Article
Language:English
Published: Thieme Medical and Scientific Publishers Pvt. Ltd. 2021-01-01
Series:Indian Journal of Radiology and Imaging
Subjects:
Online Access:http://www.thieme-connect.de/DOI/DOI?10.4103/ijri.IJRI_777_20
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spelling doaj-494ef7c497144ed5b1d0991f4879cd782021-07-15T16:29:30ZengThieme Medical and Scientific Publishers Pvt. Ltd.Indian Journal of Radiology and Imaging0971-30261998-38082021-01-0131S87S9310.4103/ijri.IJRI_777_20Radiographic findings in COVID-19: Comparison between AI and radiologistArsh Sukhija0Mangal Mahajan1Priscilla C Joshi2John Dsouza3Nagesh DN Seth4Karamchand H Patil5Departments of Radiodiagnosis and Imaging and Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaDepartments of Radiodiagnosis and Imaging and Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaDepartments of Radiodiagnosis and Imaging and Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaDepartments of Radiodiagnosis and Imaging and Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaDepartments of Radiodiagnosis and Imaging and Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaDepartments of Community Medicine, Bharati Vidyapeeth Deemed to be University Medical College and Hospital, Pune, Maharashtra, IndiaContext: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. Aim: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. Subjects and Methods: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. Results: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist’s prediction was found to be superior to that of the AI with a P VALUE of 0.005. Conclusion: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.http://www.thieme-connect.de/DOI/DOI?10.4103/ijri.IJRI_777_20artificial intelligencechest radiographscovid pneumoniarapid triaging
collection DOAJ
language English
format Article
sources DOAJ
author Arsh Sukhija
Mangal Mahajan
Priscilla C Joshi
John Dsouza
Nagesh DN Seth
Karamchand H Patil
spellingShingle Arsh Sukhija
Mangal Mahajan
Priscilla C Joshi
John Dsouza
Nagesh DN Seth
Karamchand H Patil
Radiographic findings in COVID-19: Comparison between AI and radiologist
Indian Journal of Radiology and Imaging
artificial intelligence
chest radiographs
covid pneumonia
rapid triaging
author_facet Arsh Sukhija
Mangal Mahajan
Priscilla C Joshi
John Dsouza
Nagesh DN Seth
Karamchand H Patil
author_sort Arsh Sukhija
title Radiographic findings in COVID-19: Comparison between AI and radiologist
title_short Radiographic findings in COVID-19: Comparison between AI and radiologist
title_full Radiographic findings in COVID-19: Comparison between AI and radiologist
title_fullStr Radiographic findings in COVID-19: Comparison between AI and radiologist
title_full_unstemmed Radiographic findings in COVID-19: Comparison between AI and radiologist
title_sort radiographic findings in covid-19: comparison between ai and radiologist
publisher Thieme Medical and Scientific Publishers Pvt. Ltd.
series Indian Journal of Radiology and Imaging
issn 0971-3026
1998-3808
publishDate 2021-01-01
description Context: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. Aim: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. Subjects and Methods: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. Results: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist’s prediction was found to be superior to that of the AI with a P VALUE of 0.005. Conclusion: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.
topic artificial intelligence
chest radiographs
covid pneumonia
rapid triaging
url http://www.thieme-connect.de/DOI/DOI?10.4103/ijri.IJRI_777_20
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