Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Zhang, Kang; Liu, Xiaohong; Shen, Jun; Li, Zhihuan; Sang, Ye; Wu, Xingwang; Zha, Yunfei; Liang, Wenhua; Wang, Chengdi; Wang, Ke; Ye, Linsen; Gao, Ming; Zhou, Zhongguo; Li, Liang; Wang, Jin; Yang, Zehong; Cai, H.; Xu, Jie; Yang, Lei; Cai, Wenjia; Xu, W.; Wu, Shaoxu; Zhang, Wei; Jiang, Shanping; Zheng, Lianghong; Zhang, Xuan; Wang, Li; Liu, Lu; Li, Jiaming; Yin, Haiping; Wang, Winston; Li, Oulan; Zhang, Charlotte; Liang, Liang; Wu, Tao; Deng, Ruiyun; Kang, Wei; Zhou, Yong; Chen, Ting; Lau, Johnson Yiu Nam; Fok, Manson; He, Jianxing; Lin, Tianxin; Liu, Weimin; Wang, Guangyu

doi:10.1016/j.cell.2020.04.045

Cited by 776 publications

(876 citation statements)

References 37 publications

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“…For testing, 40,880 images from 260 patients were used. The overall accuracy of the proposed model is ~ 92% [19]. In another study on CT images, the AI model developed was 87% accuracy on independent test data [20].…”

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confidence: 86%

COVID-19 detection in Chest X-ray Images using Deep Learning

Erdem¹,

Aydın²

2020

Preprint

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COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

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confidence: 86%

COVID-19 detection in Chest X-ray Images using Deep Learning

Erdem¹,

Aydın²

2020

Preprint

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“…These AI-based concepts range from protein structure prediction , drug target prediction 17 , knowledge sharing 18 , tools for population control 19,20 to the assistance of healthcare personnel, e.g. by developing AI-based coronavirus diagnostic software 21,22 . Considering the more clinically oriented AI-based technical solutions, any such progress might also induce improvements for a variety of deadly diseases including other major infections or cancer 23 .…”

Section: Introductionmentioning

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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“…To mitigate the burden on radiologists, while providing the highest quality care for patients, there has been tremendous effort to develop novel image processing approaches using machine learning algorithms 24 , particularly for COVID-19 diagnosis and prognosis 25 . These artificial intelligence (AI) models exploit and build upon medical imaging modalities such as chest CT scans [26][27][28][29][30][31][32] , chest radiographs [33][34][35][36][37][38][39][40] , and lung ultrasound 41 However, for any of these AI models to be useful in assisting clinicians in the care of COVID-19 patients, they require a robust and reliable AI deployment system 42 . Deployment is often a difficult step because clinical radiology infrastructure is not designed for easily embedding third-party systems, and doing so while maintaining context sensitivity and seamlessly embedding such systems into the radiologist workflow requires knowledge of hospital information system integration standards and often product-specific knowledge.…”

Section: Introductionmentioning

confidence: 99%

Clinical deployment and validation of a radiology artificial intelligence system for COVID-19

Hashemian

Manchanda

et al. 2020

Preprint

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The global COVID-19 pandemic has disrupted patient care delivery in healthcare systems world-wide. For healthcare providers to better allocate their resources and improve the care for patients with severe disease, it is valuable to be able to identify those patients with COVID-19 who are at higher risk for clinical complications. This may help to optimize clinical workflow and more efficiently allocate scarce medical resources. To this end, medical imaging shows great potential and artificial intelligence (AI) algorithms have been developed to assist in diagnosing and risk stratifying COVID-19 patients. However, despite the rapid development of numerous AI models, these models cannot be clinically useful unless they can be deployed in real-world environments in real-time on clinical data. Here, we propose an end-to-end AI hospital-deployment architecture for COVID-19 medical imaging algorithms in hospitals. We have successfully implemented this system at our institution and it has been used in prospective clinical validation of a deep learning algorithm potentially useful for triaging of patients with COVID-19. We demonstrate that many orchestration processes are required before AI inference can be performed on a radiology studies in real-time with the AI model being just one of the components that make up the AI deployment system. We also highlight that failure of any one of these processes can adversely affect the model's performance.

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Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Abstract: Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

Cited by 776 publications

References 37 publications

COVID-19 detection in Chest X-ray Images using Deep Learning

COVID-19 detection in Chest X-ray Images using Deep Learning

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

Clinical deployment and validation of a radiology artificial intelligence system for COVID-19

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