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Association of AI quantified COVID-19 chest CT and patient outcome.
Int J Comput Assist Radiol Surg. 2021 Mar; 16(3):435-445.IJ

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.

Authors+Show Affiliations

Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.Department of Radiology, Massachusetts General Hospitals, Harvard Medical School, Boston, MA, 02114, USA.Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.Department of Radiology, Massachusetts General Hospitals, Harvard Medical School, Boston, MA, 02114, USA.Department of Radiology, Massachusetts General Hospitals, Harvard Medical School, Boston, MA, 02114, USA.Department of Radiology, Massachusetts General Hospitals, Harvard Medical School, Boston, MA, 02114, USA. mkalra@mgh.harvard.edu.Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA. yanp2@rpi.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33484428

Citation

Fang, Xi, et al. "Association of AI Quantified COVID-19 Chest CT and Patient Outcome." International Journal of Computer Assisted Radiology and Surgery, vol. 16, no. 3, 2021, pp. 435-445.
Fang X, Kruger U, Homayounieh F, et al. Association of AI quantified COVID-19 chest CT and patient outcome. Int J Comput Assist Radiol Surg. 2021;16(3):435-445.
Fang, X., Kruger, U., Homayounieh, F., Chao, H., Zhang, J., Digumarthy, S. R., Arru, C. D., Kalra, M. K., & Yan, P. (2021). Association of AI quantified COVID-19 chest CT and patient outcome. International Journal of Computer Assisted Radiology and Surgery, 16(3), 435-445. https://doi.org/10.1007/s11548-020-02299-5
Fang X, et al. Association of AI Quantified COVID-19 Chest CT and Patient Outcome. Int J Comput Assist Radiol Surg. 2021;16(3):435-445. PubMed PMID: 33484428.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Association of AI quantified COVID-19 chest CT and patient outcome. AU - Fang,Xi, AU - Kruger,Uwe, AU - Homayounieh,Fatemeh, AU - Chao,Hanqing, AU - Zhang,Jiajin, AU - Digumarthy,Subba R, AU - Arru,Chiara D, AU - Kalra,Mannudeep K, AU - Yan,Pingkun, Y1 - 2021/01/23/ PY - 2020/09/20/received PY - 2020/12/10/accepted PY - 2021/1/24/pubmed PY - 2021/3/23/medline PY - 2021/1/23/entrez KW - Artificial intelligence KW - COVID-19 KW - Chest CT KW - Patient outcome KW - Severity scoring SP - 435 EP - 445 JF - International journal of computer assisted radiology and surgery JO - Int J Comput Assist Radiol Surg VL - 16 IS - 3 N2 - 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. SN - 1861-6429 UR - https://www.unboundmedicine.com/medline/citation/33484428/Association_of_AI_quantified_COVID_19_chest_CT_and_patient_outcome_ L2 - https://dx.doi.org/10.1007/s11548-020-02299-5 DB - PRIME DP - Unbound Medicine ER -