  Background: For acute type-A aortic dissection (ATAAD) surgery, early
post-surgery assessment is crucially important for effective treatment plans,
underscoring the need for a framework to identify the risk level of aortic
dissection cases. We examined true-lumen narrowing during follow-up
examinations, collected morphological data 14 days (early stages) after
surgery, and assessed patient risk levels over 2.8 years.
  Purpose: To establish an implementable framework supported by mathematical
techniques to predict the risk of aortic dissection patients experiencing
true-lumen narrowing after ATAAD surgery.
  Materials and Methods: This retrospective study analyzed CT data from 21
ATAAD patients. Forty uniformly distributed cross-sectional shapes (CSSs) are
derived from each lumen to account for gradual changes in shape. We introduced
the form factor (FF) to assess CSS morphology. Linear discriminant analysis
(LDA) is used for the risk classification of aortic dissection patients.
Leave-one-patient-out cross-validation (LOPO-CV) is used for risk prediction.
  Results: For this investigation, we examined data of 21 ATAAD patients
categorized into high-risk, medium-risk, and low-risk cases based on clinical
observations of the range of true-lumen narrowing. Our risk classification
machine-learning (ML) model preserving the model's generalizability. The
model's predictions reliably identified low-risk patients, thereby potentially
reducing hospital visits. It also demonstrated proficiency in accurately
predicting the risk for all high-risk patients.
  Conclusion: The suggested method anticipates the risk linked to aortic
enlargement in patients with a narrowing true lumen in the early stage
following ATAAD surgery, thereby aiding follow-up doctors in enhancing patient
care.
