About: AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT

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About: AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT
Creator	Hsieh, Ben Hu, Ping-Feng Jiang, Xiao-Long Liao, Wei-Hua Mei, Ji Pan, Ian Shi, Lin-Bo Wang, Dong-Cui Zeng, Qiu-Hua Chang, Ken Tran, My Atalay, Michael Bai, Harrison Choi, Whae Fu, Fei-Xian Halsey, Huang, Raymond Ji, B Li, Yi-Hui Sebro, Ronnie Thi, B Wang, Robin Yu, Qi-Zhi Zeng Xiong, B
source	Medline; PMC
abstract	BACKGROUND: COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy. PURPOSE: To establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologist performance without and with AI assistance. METHODS: 521 patients with positive RT-PCR for COVID-19 and abnormal chest CT findings were retrospectively identified from ten hospitals from January 2020 to April 2020. 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia on chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by two-layer fully-connected neural network to pool slices together. Our final cohort of 1,186 patients (132,583 CT slices) was divided into training, validation and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance on separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. RESULTS: Our final model achieved a test accuracy of 96% (95% CI: 90-98%), sensitivity 95% (95% CI: 83-100%) and specificity of 96% (95% CI: 88-99%) with Receiver Operating Characteristic (ROC) AUC of 0.95 and Precision-Recall (PR) AUC of 0.90. On independent testing, our model achieved an accuracy of 87% (95% CI: 82-90%), sensitivity of 89% (95% CI: 81-94%) and specificity of 86% (95% CI: 80-90%) with ROC AUC of 0.90 and PR AUC of 0.87. Assisted by the models’ probabilities, the radiologists achieved a higher average test accuracy (90% vs. 85%, Δ=5, p<0.001), sensitivity (88% vs. 79%, Δ=9, p<0.001) and specificity (91% vs. 88%, Δ=3, p=0.001). CONCLUSION: AI assistance improved radiologists' performance in distinguishing COVID-19 from non-COVID-19 pneumonia on chest CT.
has issue date	2020-04-27(xsd:dateTime)
bibo:doi	10.1148/radiol.2020201491
bibo:pmid	32339081
has license	no-cc
sha1sum (hex)	587f2c464a1af8d4c40daaf11df0a074f940bef1
schema:url	https://doi.org/10.1148/radiol.2020201491
resource representing a document's title	AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT
has PubMed Central identifier	PMC7233483
has PubMed identifier	32339081
schema:publication	Radiology
resource representing a document's body	covid:587f2c464a1af8d4c40daaf11df0a074f940bef1#body_text
is schema:about of	named entity 'ASSISTANCE' named entity 'COVID-19' named entity 'COVID-19' named entity 'pneumonia' named entity 'chest CT' named entity 'COVID' named entity 'Thoracic' named entity 'COVID-19' named entity 'COVID-19' named entity 'radiologist' named entity 'batch normalization' named entity 'gold standard' named entity 'Tensorflow' named entity 'Mann-Whitney U test' named entity 'pneumonia' named entity 'asymptomatic carriers' named entity 'RT-PCR' named entity 'clinical algorithms' named entity 'COVID-19' named entity 'radiologist' named entity 'COVID-19' named entity 'ImageNet' named entity 'COVID-19' named entity 'etiology' named entity 'pathogen' named entity 'CT scans' named entity 'sensitivity, specificity' named entity 'area under the curve' named entity 'chest CT' named entity 'search engine' named entity 'pneumonia' named entity 'COVID-19' named entity 'pneumonia' named entity '8-bit' named entity 'NVIDIA' named entity 'deep learning' named entity 'gold standard' named entity 'COVID-19' named entity 'HXB' named entity 'active contour' named entity 'Current literature' named entity 'neuron' named entity 'convolutional neural network' named entity 'COVID-19' named entity 'radiologists' named entity 'radiologists' named entity 'lung' named entity 'CTs' named entity 'pneumonia' named entity 'pneumonia' named entity 'pneumonia' named entity 'learning rate' named entity 'pneumonia' named entity '3D Slicer' named entity 'radiologists' named entity 'diagnostic radiology' named entity 'lung' named entity 'radiologist'

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