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Related Concept Videos

Aneurysm I: Introduction01:30

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An aortic aneurysm is a localized outpouching or dilation at a weak point in the artery wall. It may involve different parts of the aorta, such as the abdominal aorta, aortic arch, or thoracic aorta.Etiological factorsSeveral disorders are associated with aortic aneurysms.Congenital causes, such as primary connective tissue disorders like Marfan syndrome, impact the integrity and strength of connective tissues, notably affecting the aorta. Marfan syndrome is a genetic disorder that specifically...
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Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...
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Aneurysm management involves either conservative medical therapy or surgical intervention, depending on the size and symptoms of the aneurysm. Conservative management is generally reserved for smaller, asymptomatic aneurysms, while larger or symptomatic aneurysms often necessitate surgical repair.Conservative Medical TherapyFor small, asymptomatic aneurysms, particularly abdominal aortic aneurysms (AAA) less than 5.5 centimeters in diameter, conservative medical therapy is recommended. This...
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Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm.

Timothy K Chung1, Nathan L Liang2,3, David A Vorp1,4,5,2,6,7,8

  • 1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.

Applications in Engineering Science
|September 15, 2023
PubMed
Summary

Artificial intelligence (AI) can now predict abdominal aortic aneurysm (AAA) wall stresses, improving patient-specific rupture risk assessment. This automated method significantly reduces analysis time compared to traditional biomechanical approaches.

Keywords:
Abdominal aortic aneurysmArtificial intelligenceAutomationFinite element analysisMachine learningStress analysis

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Abdominal aortic aneurysms (AAA) pose a significant rupture risk, the 13th leading cause of death in the US.
  • Current clinical guidelines (e.g., 5.5 cm diameter) for AAA intervention are insufficient, as smaller aneurysms can also rupture.
  • Traditional biomechanical analysis for AAA rupture risk is time-consuming and requires specialized expertise, limiting clinical application.

Purpose of the Study:

  • To develop and present an automated, artificial intelligence (AI)-based methodology for predicting patient-specific biomechanical wall stresses in abdominal aortic aneurysms (AAA).
  • To overcome the limitations of traditional biomechanical analyses by significantly reducing the time and expertise required for AAA assessment.

Main Methods:

  • Utilized AI algorithms for automated segmentation of regions of interest from medical images of AAAs.
  • Employed machine learning models to predict AAA wall stresses.
  • Compared the time efficiency of the AI-based approach against traditional biomechanical analysis methods.

Main Results:

  • The AI-based methodology successfully predicted biomechanical wall stresses for individual AAAs.
  • The automated AI approach achieved prediction times of approximately 20 seconds, a substantial reduction from the ~4 hours required by traditional methods.
  • Demonstrated the potential of AI to increase the throughput of AAA biomechanical analyses.

Conclusions:

  • The developed AI-based methodology offers a faster and more efficient approach to predict AAA biomechanical wall stresses.
  • This automated tool can aid in the development of more reliable, patient-specific clinical management strategies for abdominal aortic aneurysms.
  • The significant time reduction achieved by AI facilitates broader clinical translation of biomechanical insights for AAA rupture risk assessment.