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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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Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography.

Giovanni Spinella1,2, Alice Fantazzini3, Alice Finotello4

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Journal of Digital Imaging
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Summary
This summary is machine-generated.

A deep learning pipeline accurately screens for abdominal aortic aneurysms (AAA) using computed tomography angiography (CTA) scans. This automated method shows high accuracy and sensitivity, paving the way for clinical use in AAA screening.

Keywords:
Abdominal aortic aneurysm (AAA)Artificial intelligence (AI)Deep learning (DL)Screening

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Screening

Background:

  • Abdominal aortic aneurysms (AAA) are a significant cause of mortality.
  • Early detection through screening is crucial for effective management.
  • Computed tomography angiography (CTA) is a key imaging modality for AAA diagnosis.

Purpose of the Study:

  • To validate a fully automated deep learning (DL) pipeline for AAA screening.
  • To assess the accuracy of DL-based segmentation of aorta lumen and thrombus in CTA scans.
  • To compare automated measurements with manual readings for AAA detection.

Main Methods:

  • A 2.5D convolutional neural network (CNN) architecture was employed for automated segmentation.
  • The pipeline was retrospectively evaluated on 73 thoraco-abdominal CTA scans (48 AAA, 25 controls).
  • Automated maximum aortic diameter measurements were compared against radiologist's manual measurements.

Main Results:

  • The DL pipeline achieved 97% accuracy, 98% sensitivity, and 96% specificity in classifying AAA.
  • The automated system correctly identified 47 out of 48 AAA cases and 24 out of 25 control cases.
  • The median error for automated maximum abdominal diameter measurement was 1.3 mm.

Conclusions:

  • The DL-based screening pipeline demonstrates feasibility and high accuracy for AAA detection.
  • Automated segmentation and measurement show promising results for clinical application.
  • Further validation with larger datasets is recommended for widespread clinical adoption.