COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.
The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an incre...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2100 - 2113 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950866&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950866 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950866 164569246 171950866 171950866 10.1007/s10278-023-00861-6 171950866 ppf: 2100 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model. aug: au: Nizam, Nusrat Binta Siddiquee, Sadi Mohammad Shirin, Mahbuba Bhuiyan, Mohammed Imamul Hassan Hasan, Taufiq affil: https://ror.org/05a1qpv97 mHealth Research Group, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), 1205, Dhaka, Bangladesh sug: subj: COVID-19 Pneumonia, Viral Severity of Illness Evaluation Radiography, Thoracic Deep Learning Utilization Prediction Models Lung Anatomy and Histology Funding Source Human Regression Models, Statistical Image Interpretation, Computer Assisted Descriptive Statistics Digital Imaging ab: The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled radiologists, automated assessment of COVID-19 severity using medical image analysis has become increasingly important. Chest x-ray (CXR) imaging plays a significant role in assessing the severity of pneumonia, especially in low-resource hospitals, and is the most frequently used diagnostic imaging in the world. Previous methods that automatically predict the severity of COVID-19 pneumonia mainly focus on feature pooling from pre-trained CXR models without explicitly considering the underlying human anatomical attributes. This paper proposes an anatomy-aware (AA) deep learning model that learns the generic features from x-ray images considering the underlying anatomical information. Utilizing a pre-trained model and lung segmentation masks, the model generates a feature vector including disease-level features and lung involvement scores. We have used four different open-source datasets, along with an in-house annotated test set for training and evaluation of the proposed method. The proposed method improves the geographical extent score by 11% in terms of mean squared error (MSE) while preserving the benchmark result in lung opacity score. The results demonstrate the effectiveness of the proposed AA model in COVID-19 severity prediction from chest X-ray images. The algorithm can be used in low-resource setting hospitals for COVID-19 severity prediction, especially where there is a lack of skilled radiologists. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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