Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT

Autor: Thi My Linh Tran, Ping-Feng Hu, Yi-Hui Li, Zeng Xiong, Ji Mei, Ben Hsieh, Qizhi Yu, Dongcui Wang, Fei-Xian Fu, Ronnie Sebro, Weihua Liao, Ji Whae Choi, Raymond Y. Huang, Kasey Halsey, Lin-Bo Shi, Michael K. Atalay, Harrison X. Bai, Xiao-Long Jiang, Ken Chang, Qiuhua Zeng, Ian Pan, Robin Wang
Rok vydání: 2020
Předmět:
Male
Chest ct
030218 nuclear medicine & medical imaging
0302 clinical medicine
Child
Lung
Aged
80 and over
Philadelphia
Middle Aged
Child
Preschool
030220 oncology & carcinogenesis
Female
Radiography
Thoracic
Radiology
Coronavirus Infections
Adult
China
2019-20 coronavirus outbreak
medicine.medical_specialty
Adolescent
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Pneumonia
Viral
Sensitivity and Specificity
Diagnosis
Differential
Betacoronavirus
Young Adult
03 medical and health sciences
Artificial Intelligence
health services administration
Radiologists
medicine
Humans
Radiology
Nuclear Medicine and imaging
Pandemics
Aged
Retrospective Studies
SARS-CoV-2
business.industry
Infant
Newborn
COVID-19
Infant
Rhode Island
Pneumonia
medicine.disease
Infant newborn
Artificial intelligence
Differential diagnosis
Tomography
X-Ray Computed
business
Original Research—Thoracic Imaging
Zdroj: Radiology
ISSN: 1527-1315
0033-8419
DOI: 10.1148/radiol.2020201491
Popis: Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at 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 a two-layer fully connected neural network to pool slices together. The final cohort of 1186 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 in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5
Databáze: OpenAIRE
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