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Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.
Mortani Barbosa, Eduardo J; Gefter, Warren B; Ghesu, Florin C; Liu, Siqi; Mailhe, Boris; Mansoor, Awais; Grbic, Sasa; Vogt, Sebastian.
  • Mortani Barbosa EJ; From the Perelman School of Medicine, University of Pennsylvania, Philadelphia.
  • Gefter WB; From the Perelman School of Medicine, University of Pennsylvania, Philadelphia.
  • Ghesu FC; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
  • Liu S; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
  • Mailhe B; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
  • Mansoor A; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
  • Grbic S; Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
  • Vogt S; X-Ray Products, Siemens Healthineers, Malvern, PA.
Invest Radiol ; 56(8): 471-479, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1043316
ABSTRACT

OBJECTIVES:

The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND

METHODS:

We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors.

RESULTS:

Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively.

CONCLUSIONS:

Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Radiography, Thoracic / Tomography, X-Ray Computed / Radiologists / Deep Learning / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans / Male Language: English Journal: Invest Radiol Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Radiography, Thoracic / Tomography, X-Ray Computed / Radiologists / Deep Learning / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans / Male Language: English Journal: Invest Radiol Year: 2021 Document Type: Article