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Leaflet thrombosis automatic identification in transcatheter aortic valves using 4DCT.

Laura Busto1, César Veiga1, Carlos Martínez1

  • 1Cardiology Research Group, Galicia Sur Health Research Institute, 36312, Vigo, Spain; AI Platform for Biomedical Analysis, Galicia Sur Health Research Institute, 36312, Vigo, Spain.

Computers in Biology and Medicine
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Automated deep learning models accurately detect leaflet thrombosis (LT), a complication of transcatheter aortic valve implantation (TAVI). This approach enhances early diagnosis and improves long-term transcatheter heart valve durability.

Keywords:
Four-dimensional computerized tomography (4DCT)Leaflet thrombosis (LT)Nn-UNetSegmentationTranscatheter aortic valve implantation (TAVI)

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Area of Science:

  • Cardiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Engineering

Background:

  • Leaflet thrombosis (LT) is a major complication following transcatheter aortic valve implantation (TAVI), affecting patient outcomes and the long-term durability of transcatheter heart valves (THVs).
  • Subclinical LT (SLT), characterized by hypo-attenuated leaflet thickening (HALT) and reduced leaflet motion (RELM), is difficult to diagnose manually due to observer variability.
  • Current computed tomography (CT) assessments for LT are labor-intensive and inconsistent, posing challenges as TAVI is increasingly used in younger populations.

Purpose of the Study:

  • To develop and evaluate automated segmentation models using the nnU-Net architecture for detecting and characterizing thrombi in 4D CT scans of TAVI patients.
  • To reduce manual effort and observer variability in the diagnosis of leaflet thrombosis.
  • To explore the potential of dynamic 4D CT analysis for improved LT diagnostics and personalized anticoagulation strategies.

Main Methods:

  • Manual annotation of a dataset of 4D CT scans from TAVI patients.
  • Training and implementation of eight distinct nnU-Net models for thrombus segmentation.
  • Evaluation of model performance using segmentation metrics and clinical thrombus data.

Main Results:

  • Several automated segmentation models achieved precision values greater than 0.8 for detecting leaflet thrombosis.
  • Observed variations in thrombus volume throughout the cardiac cycle indicate the importance of phase selection for accurate LT assessment.
  • The study demonstrates the feasibility of automated LT detection and characterization, reducing diagnostic challenges.

Conclusions:

  • Automated segmentation using nnU-Net shows significant potential for accurate and efficient detection of leaflet thrombosis in TAVI patients.
  • Dynamic 4D CT analysis may enhance LT diagnostic accuracy and inform personalized treatment strategies.
  • This work provides a foundation for developing predictive biomarkers and improving long-term THV outcomes through early LT detection.