COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Maayan Frid-Adar; Rula Amer; Ophir Gozes; Jannette Nassar; Hayit Greenspan

doi:10.1109/JBHI.2021.3069169

COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Maayan Frid-Adar, Rula Amer, Ophir Gozes, Jannette Nassar, Hayit Greenspan

Department of Bio-Medical Engineering

Tel Aviv University

Research output: Contribution to journal › Article › peer-review

Abstract

This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a 'Pneumonia Ratio' which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.

Original language	English
Article number	9387536
Pages (from-to)	1892-1903
Number of pages	12
Journal	IEEE Journal of Biomedical and Health Informatics
Volume	25
Issue number	6
DOIs	https://doi.org/10.1109/JBHI.2021.3069169
State	Published - Jun 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
DRR
detection
localization
patient monitoring
pneumonia
severity scoring

All Science Journal Classification (ASJC) codes

Computer Science Applications
Health Informatics
Electrical and Electronic Engineering
Health Information Management

Access to Document

10.1109/JBHI.2021.3069169

Cite this

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abstract = "This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a 'Pneumonia Ratio' which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.",

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AU - Greenspan, Hayit

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COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Abstract

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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