Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study

Chen, Jun; Wu, Lianlian; Zhang, Jun; Zhang, Liang; Gong, Dexin; Zhao, Yilin; Hu, Shan; Wang, Yonggui; Hu, Xiao; Zheng, Binbin; Zhang, Kuo; Wu, Huiling; Dong, Zaili; Xu, Youming; Zhu, Yi; Chen, Xi; Yu, Lilei; Yu, Honggang

doi:10.1101/2020.02.25.20021568

Cited by 306 publications

(307 citation statements)

References 22 publications

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“…Barstugan, Ozkaya, et al 31 unclear unclear unclear high Chen, Wu, et al 26 high unclear low high *1 Gozes, Frid-Adar, et al 25 unclear unclear high high Jin, Chen, et al 11 high unclear unclear high *2 Jin, Wang, et al 33 high unclear high high *1 Li, Qin, et al 34 low unclear low high Shan, Gao, et al 28 unclear unclear high high *2 Shi, Xia, et al 36 high unclear low high Wang, Kang, et al 29 high unclear low high Xu, Jiang, et al 27 high unclear high high Ying, Zheng, et al 23 unclear unclear low high Zheng, Deng, et al 38 unclear unclear high high…”

Section: Diagnostic Imagingmentioning

confidence: 99%

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants

Calster

Bonten

et al. 2020

Preprint

167

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words)Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST.Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China.Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in

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Section: Diagnostic Imagingmentioning

confidence: 99%

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants

Calster

Bonten

et al. 2020

Preprint

167

View full text Add to dashboard Cite

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“…As a very recent disease, we have not yet found AI studies for COVID-19 diagnosis in peer-reviewed publications, but a few reports about COVID-19 diagnosis algorithms based on chest CT in preprint form [14,15]. Wang et al [14] describe a COVID-19 diagnosis system with specificity of 67% and sensitivity of 74% on 216 slices extracted from CT volumes of patients (the whole dataset consists of 44 positive and 55 negative cases, but split strategy of dataset is unclear).…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [14] describe a COVID-19 diagnosis system with specificity of 67% and sensitivity of 74% on 216 slices extracted from CT volumes of patients (the whole dataset consists of 44 positive and 55 negative cases, but split strategy of dataset is unclear). Chen et al [15] describe a COVID-19 diagnosis system with a performance comparable to that of an expert radiologist, however the system is validated based on a quite small dataset with only 19 confirmed COVID-19 patients and only one radiologist is compared. Clearly, the development and rigorous testing of COVID-19 diagnosis algorithms remains an open topic.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of an AI System for COVID-19 Diagnosis

Cheng

Chen

Cao

et al. 2020

Preprint

152

125

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Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https : //github.com

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“…Do we still wait for collecting fairly large amount of data? Deep Learning (DL), as an example, requires a large amount of data to be trained [14,15]. The primary idea behind the use of DL is not only to avoid feature engineering but also to extract tiny features in radiology data (pixel-level nodule, for example) [16].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

2020

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The novel coronavirus outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AIdriven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

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Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study

Cited by 306 publications

References 22 publications

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Development and Evaluation of an AI System for COVID-19 Diagnosis

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

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