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<p>Volumetric assessment of abdominal aortic aneurysms (AAA) offers precise pre- and post-endovascular aortic repair (EVAR) evaluation but is laborious. The primary aim was to train and validate a network facilitating automated segmentation and volume determination of pre- and post-EVAR infrarenal AAAs displayed on computed tomography angiographies (CTA). Secondary aim was evaluation of workflow acceleration. Model was trained on ground truth segmentations. Internal and external validation was performed. AI-generated volumes of total aneurysm, lumen, and thrombus were correlated with ground truth. Model-enabled efficiency gains and semi-automatic AAA segmentations performed by three surgeons were measured. For total aneurysm, mean Dice similarity coefficient was 0.972 ± 0.013 and 0.960 ± 0.035 in internal and external validation. AI-generated thrombus volumes showed a very strong correlation with ground truth in internal (<i>r</i> = 0.996) and external validation (<i>r</i> = 0.940). Mean algorithm-facilitated time savings of 117.1 seconds (56.0%) were demonstrated for total aneurysm. Our institution-agnostic network enables automated volumetric analysis of AAAs.</p>

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Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • David Weiss,
  • Thomas Hager,
  • MingDe Lin,
  • Durga Sritharan,
  • Khaled Bousabarah,
  • Daniel Renninghoff,
  • Wolfgang Holler,
  • Kathryn Simmons,
  • Johannes Haubold,
  • Sarah Loh,
  • Uwe Fischer,
  • Julius Chapiro,
  • Cornelius Deuschl,
  • Mariam Aboian,
  • Edouard Aboian,
  • Sanjay Aneja

摘要

Volumetric assessment of abdominal aortic aneurysms (AAA) offers precise pre- and post-endovascular aortic repair (EVAR) evaluation but is laborious. The primary aim was to train and validate a network facilitating automated segmentation and volume determination of pre- and post-EVAR infrarenal AAAs displayed on computed tomography angiographies (CTA). Secondary aim was evaluation of workflow acceleration. Model was trained on ground truth segmentations. Internal and external validation was performed. AI-generated volumes of total aneurysm, lumen, and thrombus were correlated with ground truth. Model-enabled efficiency gains and semi-automatic AAA segmentations performed by three surgeons were measured. For total aneurysm, mean Dice similarity coefficient was 0.972 ± 0.013 and 0.960 ± 0.035 in internal and external validation. AI-generated thrombus volumes showed a very strong correlation with ground truth in internal (r = 0.996) and external validation (r = 0.940). Mean algorithm-facilitated time savings of 117.1 seconds (56.0%) were demonstrated for total aneurysm. Our institution-agnostic network enables automated volumetric analysis of AAAs.