Tags

Type your tag names separated by a space and hit enter

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.
Lancet Digit Health. 2021 05; 3(5):e286-e294.LD

Abstract

BACKGROUND

Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19.

METHODS

We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists.

FINDINGS

1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001).

INTERPRETATION

In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19.

FUNDING

Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Authors+Show Affiliations

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Computer Science, Brown University, Providence, RI, USA.Department of Biostatistics, Brown University, Providence, RI, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.School of Computer Science and Engineering, Central South University, Changsha, China.Carina Medical, Lexington, KY, USA.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA.Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.Department of Radiology, Xiangya Hospital, Central South University, Changsha, China. Electronic address: owenliao@csu.edu.cn.Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: yong.fan@pennmedicine.upenn.edu.Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, RI, USA. Electronic address: harrison_bai@brown.edu.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33773969

Citation

Jiao, Zhicheng, et al. "Prognostication of Patients With COVID-19 Using Artificial Intelligence Based On Chest X-rays and Clinical Data: a Retrospective Study." The Lancet. Digital Health, vol. 3, no. 5, 2021, pp. e286-e294.
Jiao Z, Choi JW, Halsey K, et al. Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digit Health. 2021;3(5):e286-e294.
Jiao, Z., Choi, J. W., Halsey, K., Tran, T. M. L., Hsieh, B., Wang, D., Eweje, F., Wang, R., Chang, K., Wu, J., Collins, S. A., Yi, T. Y., Delworth, A. T., Liu, T., Healey, T. T., Lu, S., Wang, J., Feng, X., Atalay, M. K., ... Bai, H. X. (2021). Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. The Lancet. Digital Health, 3(5), e286-e294. https://doi.org/10.1016/S2589-7500(21)00039-X
Jiao Z, et al. Prognostication of Patients With COVID-19 Using Artificial Intelligence Based On Chest X-rays and Clinical Data: a Retrospective Study. Lancet Digit Health. 2021;3(5):e286-e294. PubMed PMID: 33773969.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. AU - Jiao,Zhicheng, AU - Choi,Ji Whae, AU - Halsey,Kasey, AU - Tran,Thi My Linh, AU - Hsieh,Ben, AU - Wang,Dongcui, AU - Eweje,Feyisope, AU - Wang,Robin, AU - Chang,Ken, AU - Wu,Jing, AU - Collins,Scott A, AU - Yi,Thomas Y, AU - Delworth,Andrew T, AU - Liu,Tao, AU - Healey,Terrance T, AU - Lu,Shaolei, AU - Wang,Jianxin, AU - Feng,Xue, AU - Atalay,Michael K, AU - Yang,Li, AU - Feldman,Michael, AU - Zhang,Paul J L, AU - Liao,Wei-Hua, AU - Fan,Yong, AU - Bai,Harrison X, Y1 - 2021/03/24/ PY - 2020/10/27/received PY - 2021/02/10/revised PY - 2021/02/17/accepted PY - 2021/3/29/pubmed PY - 2021/5/8/medline PY - 2021/3/28/entrez SP - e286 EP - e294 JF - The Lancet. Digital health JO - Lancet Digit Health VL - 3 IS - 5 N2 - BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health. SN - 2589-7500 UR - https://www.unboundmedicine.com/medline/citation/33773969/Prognostication_of_patients_with_COVID_19_using_artificial_intelligence_based_on_chest_x_rays_and_clinical_data:_a_retrospective_study_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(21)00039-X DB - PRIME DP - Unbound Medicine ER -