Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

Date

2020-02-01

Author

Akkoyun, Emrah
Kwon, Sebastian T.
Acar, Aybar Can
Lee, Whal
Baek, Seungik

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

Subject Keywords

Health Informatics, Computer Science Applications

URI

https://hdl.handle.net/11511/63315

Journal

COMPUTERS IN BIOLOGY AND MEDICINE

DOI

https://doi.org/10.1016/j.compbiomed.2020.103620

Collections

Graduate School of Informatics, Article

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Citation Formats

E. Akkoyun, S. T. Kwon, A. C. Acar, W. Lee, and S. Baek, “Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference,” COMPUTERS IN BIOLOGY AND MEDICINE, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63315.