Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays Article Swipe
Prashant Gidde
,
Shyam Sunder Prasad
,
Ajay Pratap Singh
,
Nitin Bhatheja
,
Satyartha Prakash
,
Prateek Singh
,
Aakash Saboo
,
Rohit Thakar
,
Salil Kumar Gupta
,
Sumeet Saurav
,
Muthukurussi Varieth Raghunandanan
,
Amritpal Singh
,
Viren Sardana
,
Harsh Mahajan
,
Arjun Kalyanpur
,
Atanendu Shekhar Mandal
,
Vidur Mahajan
,
Anurag Agrawal
,
Anjali Agrawal
,
Vasantha Kumar Venugopal
,
Sanjay Kumar Singh
,
Debasis Dash
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.10.20.20213793
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.10.20.20213793
The coronavirus disease of 2019 (COVID-19) pandemic exposed a limitation of artificial intelligence (AI) based medical image interpretation systems. Early in the pandemic, when need was greatest, the absence of sufficient training data prevented effective deep learning (DL) solutions. Even now, there is a need for Chest-X-ray (CxR) screening tools in low and middle income countries (LMIC), when RT-PCR is delayed, to exclude COVID-19 pneumonia (Cov-Pneum) requiring transfer to higher care. In absence of local LMIC data and poor portability of CxR DL algorithms, a new approach is needed. Axiomatically, it is faster to repurpose existing data than to generate new datasets. Here, we describe CovBaseAI, an explainable tool which uses an ensemble of three DL models and an expert decision system (EDS) for Cov-Pneum diagnosis, trained entirely on datasets from the pre-COVID-19 period. Portability, performance, and explainability of CovBaseAI was primarily validated on two independent datasets. First, 1401 randomly selected CxR from an Indian quarantine-center to assess effectiveness in excluding radiologic Cov-Pneum that may require higher care. Second, a curated dataset with 434 RT-PCR positive cases of varying levels of severity and 471 historical scans containing normal studies and non-COVID pathologies, to assess performance in advanced medical settings. CovBaseAI had accuracy of 87% with negative predictive value of 98% in the quarantine-center data for Cov-Pneum. However, sensitivity varied from 0.66 to 0.90 depending on whether RT-PCR or radiologist opinion was set as ground truth. This tool with explainability feature has better performance than publicly available algorithms trained on COVID-19 data but needs further improvement.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Pneumonia
Software portability
Artificial intelligence
Medicine
Machine learning
Computer science
Ground truth
Pandemic
Quarantine
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Algorithm
Disease
Internal medicine
Pathology
Infectious disease (medical specialty)
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.10.20.20213793
- https://www.medrxiv.org/content/medrxiv/early/2020/10/21/2020.10.20.20213793.full.pdf
- OA Status
- green
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3093582638
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3093582638Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.10.20.20213793Digital Object Identifier
- Title
-
Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-raysWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-21Full publication date if available
- Authors
-
Prashant Gidde, Shyam Sunder Prasad, Ajay Pratap Singh, Nitin Bhatheja, Satyartha Prakash, Prateek Singh, Aakash Saboo, Rohit Thakar, Salil Kumar Gupta, Sumeet Saurav, Muthukurussi Varieth Raghunandanan, Amritpal Singh, Viren Sardana, Harsh Mahajan, Arjun Kalyanpur, Atanendu Shekhar Mandal, Vidur Mahajan, Anurag Agrawal, Anjali Agrawal, Vasantha Kumar Venugopal, Sanjay Kumar Singh, Debasis DashList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.10.20.20213793Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2020/10/21/2020.10.20.20213793.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2020/10/21/2020.10.20.20213793.full.pdfDirect OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Pneumonia, Software portability, Artificial intelligence, Medicine, Machine learning, Computer science, Ground truth, Pandemic, Quarantine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Algorithm, Disease, Internal medicine, Pathology, Infectious disease (medical specialty), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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