AI-based analysis of CT images for rapid triage of COVID-19 patients

Xu, Qiantong; Zhan, Xianghao; Zhou, Zhen; Li, Yi‐Heng; Xie, Peiyi; Zhang, Shu; Li, Xiuli; Yu, Yizhou; Zhou, Changsheng; Zhang, Longjiang; Gevaert, Olivier; Lu, Guangming

doi:10.1038/s41746-021-00446-z

Cited by 29 publications

(23 citation statements)

References 52 publications

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“…Many hospitals utilized machine learning analyses by combining clinical, radiological, and laboratory data for the prognostication and rapid risk stratification of PCR-confirmed COVID-19 patients [18] , [19] , [20] . The severity of illness among ICU patients was stratified via different general scoring methods such as the acute physiology and chronic health evaluation (APACHE) II and IV [21] , [22] , the Simplified Acute Physiology Score (SAPS) [23] , SOFA scores [11] , or COVID-19 specific scores were set to 4 C mortality scores [24] , [25] .…”

Section: Resultsmentioning

confidence: 99%

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Elhazmi

Al‐Omari²,

Sallam³

et al. 2022

Journal of Infection and Public Health

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Section: Resultsmentioning

confidence: 99%

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Elhazmi

Al‐Omari²,

Sallam³

et al. 2022

Journal of Infection and Public Health

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“…Third, our patients were all Asians, which could potentially limit the generalizability of our AI system to other international regions. Additional validation across populations from American and European hospitals is warranted to further validate the reported performance 45 . Fourth, selection biases were resulted from choosing a subset of radiological abnormalities for prediction would lead to selection bias.…”

Section: Discussionmentioning

confidence: 97%

Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases

Wang

Ma²,

Zhang³

et al. 2022

npj Digit. Med.

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Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRDTR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRDTR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRDTR was evaluated for abnormality identification and disease diagnosis on data from two different institutions: one was an internal testing dataset from the same institution as the training data and the second was collected from an external institution to evaluate the model generalizability and robustness to an unrelated population dataset. In such a difficult multi-class diagnosis task, our system achieved the average area under the receiver operating characteristic curve (AUC) of 0.856 (95% confidence interval (CI):0.843–0.868) and 0.841 (95%CI:0.832–0.887) for abnormality identification, and 0.900 (95%CI:0.872–0.958) and 0.866 (95%CI:0.832–0.887) for major respiratory diseases’ diagnosis on CT and CXR datasets, respectively. Furthermore, to achieve a clinically actionable diagnosis, we deployed a preliminary version of DeepMRDTR into the clinical workflow, which was performed on par with senior experts in disease diagnosis, with an AUC of 0.890 and a Cohen’s k of 0.746–0.877 at a reasonable timescale; these findings demonstrate the potential to accelerate the medical workflow to facilitate early diagnosis as a triage tool for respiratory diseases which supports improved clinical diagnoses and decision-making.

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“…[15][16][17]. Many hospitals utilized machine learning-based analyses combining clinical, radiological and laboratory data for the prognostication and rapid risk strati cation of PCR con rmed COVID-19 patients [18][19][20]. The evaluation of the severity of illness for patients admitted to the ICU has been applied by different general scoring methods such as the acute physiology and chronic health evaluation (APACHE) II and IV [21,22], Simpli ed Acute Physiology Score (SAPS) [23], and SOFA scores [11] or COVID-19 speci c scores as 4C score [24].…”

Section: Discussionmentioning

confidence: 99%

Predictors of Mortality Using Machine Learning Decision Tree Algorithm in Critically Ill Adult Patients with COVID-19 Admitted to the ICU.

Elhazmi¹,

Al‐Omari²,

Sallam

et al. 2021

Preprint

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Background:The Coronavirus Disease-19 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a major cause of intensive care unit (ICU) admissions globally. Robust data of epidemiology, characteristics, and disease outcomes from different regions and populations showed considerable variations. However, limited number of reports addressed predictors of mortality utilizing machine learning methods. Herein, we aimed to describe the association and relationship of a predefined set of variables found to be predictive of 28–day ICU outcome among adults COVID-19 patients admitted to the ICU using a machine learning decision tree (DT) algorithm.Methods:This was a prospective/retrospective, multicenter cohort study from 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The primary outcome was 28-day ICU mortality. Secondary outcomes were 90-day mortality and ICU length of stay. The predictors of mortality were identified using two predictive models, the conventional logistic regression and DT analysis.Results:A total of 1468 critically ill COVID-19 patients were included. The mean age was 55.9 (SD±15.1) years, with 74% of the patients were males. The 28-day ICU mortality was 540 (36.8%), while 90-day mortality was 600 (40.9%). The multivariable logistic regression model demonstrated that the PaO2/FiO2 ratio on ICU admission and the need for intubation or vasopressors could strongly predict 28-day ICU mortality. The DT algorithm identified five variables [need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio] provided in an algorithmic fashion to predict 28-day ICU outcome. Conclusion:Five clinical predictors of 28-day ICU outcome were identified using DT algorithmic analysis of COVID-19 patients admitted to ICU. The findings of this DT analysis may be used in ICU for early identification of critically ill COVID-19 patients who are at high risk of 28-day mortality.

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AI-based analysis of CT images for rapid triage of COVID-19 patients

Cited by 29 publications

References 52 publications

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases

Predictors of Mortality Using Machine Learning Decision Tree Algorithm in Critically Ill Adult Patients with COVID-19 Admitted to the ICU.

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