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Published on: March 8, 2019
Quantification of the Thoracic Aorta and Detection of Aneurysm at CT: Development and Validation of a Fully Automatic
Fabiola Bezerra de Carvalho Macruz1, Charles Lu1, Julia Strout1
1Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, 100 Cambridge St, Boston, MA 02114 (F.B.C.M., C.L., J.S., S.D., M.Y., V.B.); Department of Cardiovascular Imaging, Massachusetts General Hospital, Boston, Mass (A.T., S.H., B.G.); and Nuance Communications, Montreal, Quebec, Canada (R.B.).
Insights
Deep learning accurately predicts thoracic aortic aneurysm dimensions and identifies aneurysms on CT scans. This system enhances diagnostic capabilities for aortic diseases using advanced machine learning algorithms.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Medicine
- Cardiovascular Imaging
Background:
- Thoracic aortic aneurysms are a significant cause of morbidity and mortality.
- Accurate measurement of aortic diameters is crucial for diagnosis and management.
- Current manual measurement methods can be time-consuming and prone to inter-observer variability.
Purpose of the Study:
- To develop and validate a deep learning system for automatic thoracic aortic segmentation.
- To predict the largest ascending and descending aortic diameters using CT.
- To identify aneurysms in the ascending and descending aorta.
Main Methods:
- A U-Net model and postprocessing algorithm were developed using 315 CT studies (Dataset A).
- The system was validated on a separate dataset of 1400 routine CT studies (Dataset B).
- System-predicted measurements were compared against reader annotations and radiology reports.
Main Results:
- Mean absolute error for automatic diameter measurements was ≤0.27 cm.
- Intraclass correlation coefficients (ICCs) for ascending and descending aorta measurements were >0.80 and ≥0.70, respectively.
- Aneurysm detection accuracy ranged from 81% to 90% compared to expert readers.
Conclusions:
- Deep learning-based system accurately predicts thoracic aortic diameters and detects aneurysms on CT.
- The developed system demonstrates high performance and potential for clinical integration.
- This AI tool can aid radiologists in the efficient and accurate assessment of thoracic aortic pathology.
Purpose:
To develop and validate a deep learning-based system that predicts the largest ascending and descending aortic diameters at chest CT through automatic thoracic aortic segmentation and identifies aneurysms in each segment.
Materials And Methods:
In this retrospective study conducted from July 2019 to February 2021, a U-Net and a postprocessing algorithm for thoracic aortic segmentation and measurement were developed by using a dataset (dataset A) that included 315 CT studies split into training, hyperparameter-tuning, and testing sets. The U-Net and postprocessing algorithm were associated with a Digital Imaging and Communications in Medicine series filter and visualization interface and were further validated by using a dataset (dataset B) that included 1400 routine CT studies. In dataset B, system-predicted measurements were compared with annotations made by two independent readers as well as radiology reports to evaluate system performance.
Results:
In dataset B, the mean absolute error between the automatic and reader-measured diameters was equal to or less than 0.27 cm for both the ascending aorta and the descending aorta. The intraclass correlation coefficients (ICCs) were greater than 0.80 for the ascending aorta and equal to or greater than 0.70 for the descending aorta, and the ICCs between readers were 0.91 (95% CI: 0.90, 0.92) and 0.82 (95% CI: 0.80, 0.84), respectively. Aneurysm detection accuracy was 88% (95% CI: 86, 90) and 81% (95% CI: 79, 83) compared with reader 1 and 90% (95% CI: 88, 91) and 82% (95% CI: 80, 84) compared with reader 2 for the ascending aorta and descending aorta, respectively.
Conclusion:
Thoracic aortic aneurysms were accurately predicted at CT by using deep learning.Keywords: Aorta, Convolutional Neural Network, Machine Learning, CT, Thorax, AneurysmsSupplemental material is available for this article.© RSNA, 2022.
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