Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model cat...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 793 - 804 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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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=184081718&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081718 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081718 184081718 184081718 10.1007/s10278-024-01151-5 184081718 ppf: 793 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset. aug: au: Zandehshahvar, Mohammadreza van Assen, Marly Kim, Eun Kiarashi, Yashar Keerthipati, Vikranth Tessarin, Giovanni Muscogiuri, Emanuele Stillman, Arthur E. Filev, Peter Davarpanah, Amir H. Berkowitz, Eugene A. Tigges, Stefan Lee, Scott J. Vey, Brianna L. De Cecco, Carlo Adibi, Ali affil: https://ror.org/01zkghx44 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA sug: subj: Deep Learning Neural Networks (Computer) Prediction Models Uncertainty Lung Diseases Classification Severity of Illness Classification Lung Diseases Radiography Severity of Illness Indices Radiography, Thoracic COVID-19 Pathology Human Adult Middle Age Aged Male Female United States South Korea Funding Source Descriptive Statistics Radiologists Consensus Validation Studies Kendall's tau Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR. 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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