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Optical coherence tomography-enabled classification of the human venoatrial junction.

Arielle S Joasil1, Aidan M Therien1, Christine P Hendon1

  • 1Columbia University, Department of Electrical Engineering, New York, United States.

Journal of Biomedical Optics
|January 22, 2025
PubMed
Summary

Automated algorithms classify cardiac tissues in optical coherence tomography (OCT) images for atrial fibrillation (AF) treatment. This advancement aids physicians in identifying targets for AF ablation, improving patient outcomes.

Keywords:
atrial fibrillationdeep learningmachine learningoptical coherence tomographyradiofrequency ablation

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Area of Science:

  • Cardiovascular Imaging and Intervention
  • Medical Artificial Intelligence
  • Computational Pathology

Background:

  • Radiofrequency ablation for atrial fibrillation (AF) targets pulmonary vein isolation but can cause complications.
  • Accurate identification of cardiac tissue boundaries, specifically venoatrial junctions, is crucial for effective AF ablation.
  • Current methods for tissue identification may lack the precision needed for complex anatomical structures.

Purpose of the Study:

  • To develop and evaluate automated classification algorithms for optical coherence tomography (OCT) volumes of human venoatrial junctions.
  • To differentiate between left atrial and pulmonary vein tissue using OCT imaging.
  • To enhance the precision of tissue characterization for guiding AF ablation procedures.

Main Methods:

  • A dataset of 26 venoatrial junction OCT volumes was analyzed.
  • Texture, statistical, and optical features were extracted from OCT patches.
  • Random forest (RF), logistic regression (LR), and convolutional neural networks (CNNs) were employed for patch classification, with CNNs utilizing both patches and derived features.

Main Results:

  • The patch-only CNN achieved the best balance of sensitivity and specificity (AUROC ).
  • Random forest (RF) demonstrated higher sensitivity than logistic regression (LR) (AUROC ).
  • Automated analysis successfully identified cardiac tissues in benchtop OCT images.

Conclusions:

  • Automated analysis of OCT images can accurately identify cardiac tissues.
  • Further refinement of algorithms using *in vivo* data is necessary for clinical translation.
  • This technology has the potential to assist physicians in identifying critical substrates for AF treatment.