Selected article for: "accuracy specificity and disease identification"
Author: Bararia, A.; Ghosh, A.; Bose, C.; Bhar, D.
Title: Network for subclinical prognostication of COVID 19 Patients from data of thoracic roentgenogram: A feasible alternative screening technology
  • Cord-id: cxkppdct
  • Document date: 2020_9_9
    • ID: cxkppdct
    Snippet: Background and Study Aim: COVID 19 is the terminology driving peoples life in the year 2020 without a supportive globally high mortality rate. Coronavirus lead pandemic is a new found disease with no gold standard diagnostic and therapeutic guideline across the globe. Amidst this scenario our aim is to develop a prediction model that makes mass screening easy on par with reducing strain on hospitals diagnostic facility and doctors alike. For this prediction model, a neural network based on Chest
    Document: Background and Study Aim: COVID 19 is the terminology driving peoples life in the year 2020 without a supportive globally high mortality rate. Coronavirus lead pandemic is a new found disease with no gold standard diagnostic and therapeutic guideline across the globe. Amidst this scenario our aim is to develop a prediction model that makes mass screening easy on par with reducing strain on hospitals diagnostic facility and doctors alike. For this prediction model, a neural network based on Chest X-ray images has been developed. Alongside the aim is also to generate a case record form that would include prediction model result along with few other subclinical factors for generating disease identification. Once found positive then only it will proceed to RT-PCR for final validation. The objective was to provide a cheap alternative to RT-PCR for mass screening and to reduced burden on diagnostic facility by keeping RT-PCR only for final confirmation. Methods: Datasets of chest X-ray images gathered from across the globe has been used to test and train the network after proper dataset curing and augmentation. Results: The final neural network-based prediction model showed an accuracy of 81% with sensitivity of 82% and specificity of 90%. The AUC score obtained is 93.7%. Discussion and Conclusion: The above results based on the existing datasets showcase our model capability to successfully distinguish patients based on Chest X-ray (a non-invasive tool) and along with the designed case record form it can significantly contribute in increasing hospitals monitoring and health care capability.

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