An Artificial Intelligence-Based Automatic Classifier for the Presence of False Lumen Thrombosis After Frozen Elephant Trunk Operation.

Objective: To develop an unsupervised artificial intelligence algorithm for identifying and quantifying the presence of false lumen thrombosis (FL) after Frozen Elephant Trunk (FET) operation in computed tomography angiographic (CTA) images in an interdisciplinary approach.

Methods: CTA datasets were retrospectively collected from eight patients after FET operation for aortic dissection from a single center. Of those, five patients had a residual aortic dissection with partial false lumen thrombosis, and three patients had no false lumen or thrombosis. Centerlines of the aortic lumen were defined, and images were calculated perpendicular to the centerline. Lumen and thrombosis were outlined and used as input for a variational autoencoder (VAE) using 2D convolutional neural networks (2D CNN). A 2D latent space was chosen to separate images containing false lumen patency, false lumen thrombosis and no presence of false lumen. Classified images were assigned a thrombus score for the presence or absence of FL thrombosis and an average score for each patient.

Results: Images reconstructed by the trained 2D CNN VAE corresponded well to original images with thrombosis. Average thrombus scores for the five patients ranged from 0.05 to 0.36 where the highest thrombus scores coincided with the location of the largest thrombus lesion. In the three patients without large thrombus lesions, average thrombus scores ranged from 0.002 to 0.01.

Conclusions: The presence and absence of a FL thrombus can be automatically classified by the 2D CNN VAE for patient-specific CTA image datasets. As FL thrombosis is an indication for positive aortic remodeling, evaluation of FL status is essential in follow-up examinations. The presented proof-of-concept is promising for the automated classification and quantification of FL thrombosis.