دورية أكاديمية
Emphysema subtyping on thoracic computed tomography scans using deep neural networks
| العنوان: | Emphysema subtyping on thoracic computed tomography scans using deep neural networks |
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| المؤلفون: | Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier, Dirk Jan Slebos, Bram van Ginneken |
| المصدر: | Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023) |
| بيانات النشر: | Nature Portfolio |
| سنة النشر: | 2023 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | https://doi.org/10.1038/s41598-023-40116-6Test; https://doaj.org/toc/2045-2322Test; https://doaj.org/article/a6088f9c73f7440ab03304b69146c115Test |
| DOI: | 10.1038/s41598-023-40116-6 |
| الإتاحة: | https://doi.org/10.1038/s41598-023-40116-6Test https://doaj.org/article/a6088f9c73f7440ab03304b69146c115Test |
| رقم الانضمام: | edsbas.A76DDBCB |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-023-40116-6 |