Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients

Shi, Wei; Peng, Xueqing; Liu, Tiefu; Cheng, Zenghui; Lu, Hongzhou; Yang, Shuyi; Zhang, Jiulong; Li, Feng; Wang, Mei; Zhang, Xinlei; Gao, Yaozong; Shi, Yuxin; Zhang, Zhiyong; Shan, Fei

doi:10.2139/ssrn.3546089

Cited by 43 publications

(53 citation statements)

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“…However, published studies have limitations such as small sample size, lack of external validation, no comparison with radiologist performance, no gold standard for the "other pneumonia" diagnosis, etc. (10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

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

Bai¹,

Wang²,

Xiong³

et al. 2020

Radiology

355

379

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AI assistance improved radiologists' performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT. Key Results: An AI model had higher test accuracy (96% vs 85%, p<0.001), sensitivity (95% vs 79%, p<0.001), and specificity (96% vs 88%, p=0.002) than radiologists. In an independent test set, our AI model achieved an accuracy of 87%, sensitivity of 89% and specificity of 86%. With AI assistance, the radiologists achieved a higher average accuracy (90% vs 85%, p<0.001), sensitivity (88% vs 79%, p<0.001) and specificity (91% vs 88%, p=0.001). AbstractBackground: 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%, ∆=5, p<0.001), sensitivity (88% vs. 79%, ∆=9, p<0.001) and specificity (91% vs. 88%, ∆=3, p=0.001).

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Section: Introductionmentioning

confidence: 99%

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

Bai¹,

Wang²,

Xiong³

et al. 2020

Radiology

355

379

View full text Add to dashboard Cite

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“…Despite promising results in recent studies [74,84], many AI models were tested in small datasets. The studies using small dataset (e.g., <300 patients) often showed high AUC or accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Except for diagnosing COVID-19, AI also shows good performance in predicting the severity of COVID-19. Shi et al [84] proposed a deep-learning-based quantitative assessment method to predict the severity of COVID-19 (severe vs. non-severe). The deep learning method calculated two indices named mass of infection (MOI) and the percentage of infection (POI), which had higher values in the severe group than the non-severe group of patients.…”

Section: Ai-based Image Analysis For Covid-19mentioning

confidence: 99%

The Role of Imaging in the Detection and Management of COVID-19: A Review

Dong

Tang

Wang

et al. 2021

IEEE Rev. Biomed. Eng.

346

247

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Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnoses and treatment assessment of the disease. Herein, we review the use of imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography -CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play an important role in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

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“…We therefore argue that systems such as Swarm Learning that allow fair, transparent and still highly regulated shared data analytics while preserving data privacy regulations are to be favored, particularly during times of high urgency to develop supportive tools for medical decision making. We therefore also propose to explore SL for image-based diagnostics of COVID-19 from patterns in X-ray images or computed tomography (CT) scans 21,22 , structured health records 67 , or wearables for disease tracking 14 and GSE122515 (dataset 3). Briefly, this dataset was generated by inspection of all publicly available datasets at GEO on September 20th, 2017.…”

Section: Discussionmentioning

confidence: 99%

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Warnat-Herresthal

Schultze

Shastry

et al. 2020

Preprint

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AbstractIdentification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

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Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients

Cited by 43 publications

References 0 publications

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

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

The Role of Imaging in the Detection and Management of COVID-19: A Review

Swarm Learning as a privacy-preserving machine learning approach for disease classification

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