Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

Autor: Youngjin Yoo, Alexander W. Sauter, Pina C. Sanelli, Philippe Grenier, Sasa Grbic, Savvas Nicolaou, Eduardo J. Mortani Barbosa, Dorin Comaniciu, Nakul Gupta, Bogdan Georgescu, François Mellot, Thomas Re, Jordi Broncano Cabrero, Valentin Ziebandt, Thomas Flohr, Gorka Bastarrika Alemañ, Guillaume Chabin, Shikha Chaganti
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
medicine.medical_specialty
Viral pneumonia
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Logistic regression
Machine learning
computer.software_genre
Machine Learning (cs.LG)
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
Text mining
Discriminative model
FOS: Electrical engineering
electronic engineering
information engineering
medicine
Humans
Radiology
Nuclear Medicine and imaging
Tomography
Retrospective Studies
Neuroradiology
SARS-CoV-2
business.industry
Image and Video Processing (eess.IV)
COVID-19
Deep learning
General Medicine
Thorax
Electrical Engineering and Systems Science - Image and Video Processing
Classification
medicine.disease
3. Good health
Random forest
Pneumonia
Imaging Informatics and Artificial Intelligence
Feature (computer vision)
030220 oncology & carcinogenesis
Artificial intelligence
Radiology
business
computer
Zdroj: European Radiology
ISSN: 1432-1084
0938-7994
Popis: Objectives To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. Methods Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning–based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. Results Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. Conclusions Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. Key Points • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)–based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07937-3.
Databáze: OpenAIRE
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