Selected article for: "accuracy sensitivity specificity and machine learning"
Author: Al-karawi, D.; Al-Zaidi, S.; Polus, N.; Jassim, S.
Title: AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients
  • Cord-id: 1zbe6t48
  • Document date: 2020_5_8
    • ID: 1zbe6t48
    Snippet: Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of the pandemic to all continents, albeit with a nonuniform community transmission, necessitates chest imaging for medical triage of patients presenting moderate-severe clinical COVID-19 features. This paper reports the development of innovative mac
    Document: Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of the pandemic to all continents, albeit with a nonuniform community transmission, necessitates chest imaging for medical triage of patients presenting moderate-severe clinical COVID-19 features. This paper reports the development of innovative machine learning schemes for the analysis of Chest X-Ray (CXR) scan images of COVID-19 patients in almost real-time, demonstrating significantly high accuracy in identifying COVID-19 infection. The performance testing was conducted on a combined dataset comprising CXRs of positive COVID-19 patients, patients with various viral and bacterial infections, as well as persons with a clear chest. The test resulted in successfully distinguishing CXR COVID-19 infection from the other cases with an average accuracy of 94.43%, sensitivity 95% and specificity 93.86%.

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