Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

Wang, Robin; Jiao, Zhicheng; Yang, Li; Choi, Ji Whae; Xiong, Zeng; Halsey, Kasey; Tran, Thi Thanh Van; Pan, Ian; Collins, Scott; Feng, Xue; Wu, Jing; Chang, Ken; Shi, Linbo; Yang, Shuai; Yu, Qizhi; Liu, Jie; Fu, Feixian; Jiang, Xiaolong; Wang, Dong-Cui; Zhu, Liping; Yi, Xiaoping; Healey, Terrance T.; Zeng, Qiuhua; Liu, Tao; Hu, Ping-Feng; Huang, Raymond Y.; Li, Yihui; Sebro, Ronnie; Zhang, Paul J.L.; Wang, Jianxin; Atalay, Michael K.; Liao, Weihua; Fan, Yong; Bai, Harrison X.

doi:10.1007/s00330-021-08049-8

Cited by 52 publications

(50 citation statements)

References 21 publications

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“…Wang et al evaluated a deep-learning model that combined CT imaging and clinical data in a multi-institutional international cohort of 1051 COVID-19 patients with the purpose to predict future deterioration to critical illness in those patients [61] . The prediction model achieved a C-index of 0,80 with an AUC of 0,82, 0,81, and 0,83 for prediction of progression risk at cutoff values of 3, 5, and 7 days, respectively.…”

Section: Role Of the Aimentioning

confidence: 99%

“…The prediction model achieved a C-index of 0,80 with an AUC of 0,82, 0,81, and 0,83 for prediction of progression risk at cutoff values of 3, 5, and 7 days, respectively. This model demonstrated the ability to successfully stratify the patients into risk score group [61] . We can speculate that an AI-trained model combining imaging-based and clinical-based data might predict the persistence of symptoms and lung progression abnormalities in long-COVID patients.…”

Section: Role Of the Aimentioning

confidence: 99%

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Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Cau

Faa

Nardi

et al. 2022

European Journal of Radiology

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SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

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Section: Role Of the Aimentioning

confidence: 99%

Section: Role Of the Aimentioning

confidence: 99%

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Cau

Faa

Nardi

et al. 2022

European Journal of Radiology

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“…Wang et al [65], developed an artificial intelligence system in a time-to-event analysis framework to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. The artificial intelligence system achieved a C-index of 0.80 for predicting individual COVID-19 patients as having critical illness, and successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).…”

Section: Application On Chest Ct Imagesmentioning

confidence: 99%

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

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“…Jiao et al 22 and Wang et al 23 used the same method, i.e. they combined results of an EfficientNet on images and a neural network on clinical data to predict severity (death, ICU, need of mechanical ventilation).…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

Chieregato

Frangiamore

Morassi

et al. 2022

Sci Rep

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COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.

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Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

Cited by 52 publications

References 21 publications

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

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