Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

Abstract SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacu...

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Autores principales:	Prashant Sadashiv Gidde, Shyam Sunder Prasad, Ajay Pratap Singh, Nitin Bhatheja, Satyartha Prakash, Prateek Singh, Aakash Saboo, Rohit Takhar, Salil Gupta, Sumeet Saurav, Raghunandanan M. V., Amritpal Singh, Viren Sardana, Harsh Mahajan, Arjun Kalyanpur, Atanendu Shekhar Mandal, Vidur Mahajan, Anurag Agrawal, Anjali Agrawal, Vasantha Kumar Venugopal, Sanjay Singh, Debasis Dash
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c7e55fe884944070b531d1f4f6b42c63
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spelling	oai:doaj.org-article:c7e55fe884944070b531d1f4f6b42c632021-12-05T12:13:27ZValidation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays10.1038/s41598-021-02003-w2045-2322https://doaj.org/article/c7e55fe884944070b531d1f4f6b42c632021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02003-whttps://doaj.org/toc/2045-2322Abstract SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.Prashant Sadashiv GiddeShyam Sunder PrasadAjay Pratap SinghNitin BhathejaSatyartha PrakashPrateek SinghAakash SabooRohit TakharSalil GuptaSumeet SauravRaghunandanan M. V.Amritpal SinghViren SardanaHarsh MahajanArjun KalyanpurAtanendu Shekhar MandalVidur MahajanAnurag AgrawalAnjali AgrawalVasantha Kumar VenugopalSanjay SinghDebasis DashNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Prashant Sadashiv Gidde Shyam Sunder Prasad Ajay Pratap Singh Nitin Bhatheja Satyartha Prakash Prateek Singh Aakash Saboo Rohit Takhar Salil Gupta Sumeet Saurav Raghunandanan M. V. Amritpal Singh Viren Sardana Harsh Mahajan Arjun Kalyanpur Atanendu Shekhar Mandal Vidur Mahajan Anurag Agrawal Anjali Agrawal Vasantha Kumar Venugopal Sanjay Singh Debasis Dash Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
description	Abstract SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.
format	article
author	Prashant Sadashiv Gidde Shyam Sunder Prasad Ajay Pratap Singh Nitin Bhatheja Satyartha Prakash Prateek Singh Aakash Saboo Rohit Takhar Salil Gupta Sumeet Saurav Raghunandanan M. V. Amritpal Singh Viren Sardana Harsh Mahajan Arjun Kalyanpur Atanendu Shekhar Mandal Vidur Mahajan Anurag Agrawal Anjali Agrawal Vasantha Kumar Venugopal Sanjay Singh Debasis Dash
author_facet	Prashant Sadashiv Gidde Shyam Sunder Prasad Ajay Pratap Singh Nitin Bhatheja Satyartha Prakash Prateek Singh Aakash Saboo Rohit Takhar Salil Gupta Sumeet Saurav Raghunandanan M. V. Amritpal Singh Viren Sardana Harsh Mahajan Arjun Kalyanpur Atanendu Shekhar Mandal Vidur Mahajan Anurag Agrawal Anjali Agrawal Vasantha Kumar Venugopal Sanjay Singh Debasis Dash
author_sort	Prashant Sadashiv Gidde
title	Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
title_short	Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
title_full	Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
title_fullStr	Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
title_full_unstemmed	Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
title_sort	validation of expert system enhanced deep learning algorithm for automated screening for covid-pneumonia on chest x-rays
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c7e55fe884944070b531d1f4f6b42c63
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Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

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