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A machine learning approach for predicting descending thoracic aortic diameter.

Ronghuang Yu1, Min Jin1,2, Yaohui Wang3

  • 1Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China.

Frontiers in Cardiovascular Medicine
|March 2, 2023
PubMed
Summary

Machine learning models accurately predict descending thoracic aorta diameters, aiding stent graft selection for Thoracic Endovascular Aortic Repair (TEVAR) patients. This improves sizing and reduces complications.

Keywords:
CTATEVARaortic diametermachine learningpredictive model

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Area of Science:

  • Cardiovascular imaging and AI
  • Medical device engineering
  • Predictive modeling in medicine

Background:

  • Descending thoracic aortic aneurysm (TAA) and dissection (TBAD) require precise stent graft sizing.
  • Accurate diameter prediction is crucial for successful Thoracic Endovascular Aortic Repair (TEVAR).
  • Current methods may lack precision in determining optimal stent graft dimensions.

Purpose of the Study:

  • To develop and validate machine learning models for predicting descending thoracic aorta diameters.
  • To provide data-driven evidence for selecting appropriate stent graft sizes in TBAD patients.
  • To reduce TEVAR-related complications through improved device selection.

Main Methods:

  • Utilized CT angiography (CTA) data from 200 patients without severe aortic deformation.
  • Reconstructed 3D aorta models and created 12 cross-sections for analysis.
  • Developed 12 predictive models using Linear Regression, Support Vector Machine, Extra-Tree Regression, and Random Forest Regression algorithms.
  • Evaluated model performance using Mean Squared Error (MSE) and Shapley values for feature importance.

Main Results:

  • Identified key predictors of aortic diameter, including age and hypertension.
  • Support Vector Machine models achieved MSE < 2 mm² with < 2 mm prediction error in 90% of cases.
  • Stent oversizing was 3 mm for dSINE patients versus 1 mm for uncomplicated cases.

Conclusions:

  • Machine learning models effectively predict descending thoracic aorta diameters.
  • These models offer valuable insights for selecting optimal stent graft sizes for TBAD patients.
  • Improved sizing through predictive modeling can decrease TEVAR complications.