2022
DOI: 10.1016/j.acra.2022.03.023
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An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

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Cited by 11 publications
(5 citation statements)
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“…However, because CT images were reviewed by radiologists and scored according to the degree of involvement, it was difficult to regard the CT severity score as a quantitative indicator. Recently, various studies have reported that the clinical outcome in COVID-19 patients can be predicted using QCT through AI software or deep learning machines [23,[40][41][42]. Our study showed that the QCT COVID scores at admission could predict rapid progression in patients with COVID-19.…”
Section: Discussionsupporting
confidence: 50%
“…However, because CT images were reviewed by radiologists and scored according to the degree of involvement, it was difficult to regard the CT severity score as a quantitative indicator. Recently, various studies have reported that the clinical outcome in COVID-19 patients can be predicted using QCT through AI software or deep learning machines [23,[40][41][42]. Our study showed that the QCT COVID scores at admission could predict rapid progression in patients with COVID-19.…”
Section: Discussionsupporting
confidence: 50%
“…Scoring systems are a different method to assess AI results, which we consider to be less precise compared to the percentage of the volume of affected lungs. Also, the patient population in the above study was smaller and less homogenous compared to ours (27). In our study, we defined the hazard rates using statistical methods, namely, regression analysis and the Youden index.…”
Section: Discussionmentioning
confidence: 89%
“…Several published studies demonstrated the feasibility of using CT findings as well as AI-based CT evaluation to predict COVID-19 pneumonia patients' outcome in various ways (17,18,19,20,21,22,23,24,25,26,27). However, to the best of our knowledge, the hazard rates based on AI-driven CT percentages of the extent of COVID-19 pneumonia on such a large dataset of patients have not been defined yet.…”
Section: Introductionmentioning
confidence: 99%
“…We observed that machine learning analyses of the variables of the severity of lung involvement in The existing literature on Covid-19 and the estimation of length of stay (LoS) was found to be limited. In a study involving 254 patients conducted by Chamberlin et al, a score other than CORADS was used to predict hospitalization and an accuracy of 0.77 was determined (24). In a different study by Purkayastha et al that used a different scoring system than CORADS, the accuracy levels of the ML analysis results using the "boosting" and "bayesian" methods were 0.68 (p=0.023) and 0.77 (p=0.023), respectively (25).…”
Section: Resultsmentioning
confidence: 99%