Association of AI quantified COVID-19 chest CT and patient outcome

Abstract

Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. Methods We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). Results AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, p < 0.001). Using AI-based scores produced significantly higher ( p < 0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. Conclusions Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

“…A total of 2/24 studies used CT imaging features (Ning et al, 2020; X. Fang et al, 2021). Particularly, Ning et al used CT images in addition to clinical features, while Fang et al developed an artificial intelligence (AI) framework using deep neural networks to segment lung lobes and pulmonary opacities, and baseline ML methods to predict mortality based on radiological severity scores (accounting for the volume ratio of pulmonary opacities in each lung lobe).…”

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

“…However, CT images are usually visually interpreted by radiologists with diverse levels of experience, which is subjective with large variability that is unable to quantitatively assess the disease severity and is also time-consuming and laborintensive. Previous studies have shown that quantitative CT is comparable or superior to visual CT score in assessment of the severity of COVID-19 (28,45,46). Recently, several studies have used quantitative CT to predict clinical outcomes via AI software in patients with COVID-19 (43,47,48).…”

“…The combination of clinical characteristics and radiomic features from CT could achieve better accuracy in prediction (18,19). Some studies also apply deep learning to automatically learn features from CT images or in combination with clinical data and radiomics for risk assessment of COVID-19 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). Deep learning and radiomics can be a more objective, quantitative, and stable system for the assessment of the COVID-19 disease course.…”

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

“…Severity assessment facilitates monitoring the COVID-19 infection course. Furthermore, it is closely related to prognosis outcomes ( Fang et al, 2021 ), and detection of high-risk patients with early intervention is highly important to lower the fatality rate of COVID-19. Thus, we reviewed AI algorithms and models proposed for COVID-19 severity assessment and prognosis prediction in one section.…”