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, Zhen; Li, Liang; Wang, Jin; Yang, Zehong; Cai, H.; Xu, Jie; Yang, Liu; 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, Tom; Deng, Ruiyun; Kang, Wei; Zhou, Yong; Chen, Ting; Lau, Johnson Y. N.; Fok, Manson; He, Jianxing; Lin, Tianxin; Liu, Weimin; Wang, Guangyu

doi:10.1016/j.cell.2020.08.029

Cited by 191 publications

(207 citation statements)

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“…Our findings may indicate that the level of lung manifestation could also be associated with a higher probability of concomitant diseases. Furthermore, this study supports previous findings in terms of association between quantification features and clinical parameters [ 8 , 27 ]. ICU and non-ICU patients significantly differed in Opacityscore.…”

Section: Discussionsupporting

confidence: 92%

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

et al. 2021

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Objectives : To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data. Methods : We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into subcohorts with or without necessity of intensive care unit (ICU) treatment. Subcohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test. Results : We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all P<0.001 if not otherwise stated) with occurrence of ICU stay (R=0.74), length of ICU stay (R=0.81), lethal outcome (R=0.56) and length of hospital stay (R=0.33, P<0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R=0.60), lactate dehydrogenase (LDH) (R=0.60), troponin (TNTHS) (R=0.55) and c-reactive protein (CRP) (R=0.51). Differences (P<0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher Opacityscore (12.50 vs. 4.96) and severity of laboratory data changes such as CRP (11.64 vs. 5.07 mg/dl, P<0.01). Conclusions : Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.

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Section: Discussionsupporting

confidence: 92%

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

et al. 2021

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“…Since the very beginning of the COVID-19 pandemic, several studies have examined the AI-based analysis of lung CT images. This was initially applied to the task of differentiating COVID-19 from other lung diseases [ 301 , 302 ], and more recently to assess its severity [ 303 ] and prognosis [ 304 , 305 ]. The initial attempts contained a limited number of cases without expert validation.…”

Section: Chest Imagingmentioning

confidence: 99%

A comprehensive review of imaging findings in COVID-19 - status in early 2021

Afshar‐Oromieh

Prosch

Schaefer-Prokop

et al. 2021

Eur J Nucl Med Mol Imaging

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Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

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“…Based on the 16 studies, the performance of the DL algorithms for detecting COVID-19 was determined and is summarized in Table 2 [22,24,33]. The pooled sensitivity and specificity of the DL methods for detecting COVID-19 was 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with a summary ROC (SROC) of 0.98 (Figure 2).…”

Section: Model Performancementioning

confidence: 99%

“…A total of 3 studies compared the performance of DL models with radiologists [22,24,33]. Zhang et al [33] included 8 radiologists with 5 to 25 years of experience; they were categorized into two groups: junior radiologists had 5 to 15 years of experience and senior radiologists had 15 to 25 years of experience. Bai et al [24] compared DL model performance with 6 radiologists; 3 of them had 10 years of experience (ie, junior) and 3 had 20 years of experience (ie, senior).…”

Section: Overviewmentioning

confidence: 99%

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Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

Poly¹,

Islam²,

Li³

et al. 2021

JMIR Med Inform

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Background The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. Objective The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. Methods A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms “COVID-19,” or “coronavirus,” or “SARS-CoV-2,” or “novel corona,” or “2019-ncov,” and “deep learning,” or “artificial intelligence,” or “automatic detection.” Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. Results A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. Conclusions Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

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

Cited by 191 publications

References 0 publications

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

A comprehensive review of imaging findings in COVID-19 - status in early 2021

Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

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