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. 2021 Mar;16(3):435-445.
doi: 10.1007/s11548-020-02299-5. Epub 2021 Jan 23.

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

Xi Fang  1 , Uwe Kruger  1 , Fatemeh Homayounieh  2 , Hanqing Chao  1 , Jiajin Zhang  1 , Subba R Digumarthy  2 , Chiara D Arru  2 , Mannudeep K Kalra  3 , Pingkun Yan  4
Affiliations

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

Xi Fang et al. Int J Comput Assist Radiol Surg. 2021 Mar.

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, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) 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.

Keywords: Artificial intelligence; COVID-19; Chest CT; Patient outcome; Severity scoring.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Segmentation results of lung lobes and pulmonary opacities. Areas colored in magenta indicate segmented lesions. Other colored areas represent the segmented lobes, with orange: right upper, green: right middle, navy: right lower, yellow: left upper and ocean: left lower
Fig. 2
Fig. 2
Correlation between the severity scores assigned by radiologists and computed by our deep learning-based segmentation on Site A dataset
Fig. 3
Fig. 3
Correlation between the severity scores assigned by radiologists and computed by our deep learning-based segmentation on Site B dataset
Fig. 4
Fig. 4
For different buckets divided by severity scores obtained by AI and radiologists, values of number of patients from different patient groups (I, II, III, IV) are presented on Site A dataset. RA: Results of radiologists’ annotation. AI: Results of the AI-based method. Heights of the bars represent the number
Fig. 5
Fig. 5
For each severity score bucket shown in vertical, horizontal stacked bars present the proportion of patient number of one severity group to patient number of all severity groups of Site A in the bucket. Widths of the bars represent such proportion (mild patient: green for group I and light green for group II; severe patient: light red for group III and red for group IV)
Fig. 6
Fig. 6
For different buckets divided by severity scores obtained by AI and radiologists, values of number of patients from different patient groups (I, II, III, IV) are presented on Site B dataset. RA: Results of radiologists’ annotation. AI: Results of the AI-based method. Heights of the bars represent the number
Fig. 7
Fig. 7
For each severity score bucket shown in vertical, horizontal stacked bars present the proportion of patient number of one severity group to patient number of all severity groups of Site B in the bucket. Widths of the bars represent such proportion (mild patient: green for group I and light green for group II; severe patient: light red for group III and red for group IV)
Fig. 8
Fig. 8
Comparison of severity scoring methods under ROC curves on Site A
Fig. 9
Fig. 9
Comparison of severity scoring methods under ROC curves on Site B
See this image and copyright information in PMC

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