Association of AI quantified COVID-19 chest CT and patient outcome.
Int J Comput Assist Radiol Surg
; 16(3): 435-445, 2021 Mar.
Article
in English
| MEDLINE | ID: covidwho-1041909
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Thorax
/
Artificial Intelligence
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Qualitative research
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Int J Comput Assist Radiol Surg
Journal subject:
Radiology
Year:
2021
Document Type:
Article
Affiliation country:
S11548-020-02299-5
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