Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning

Abstract

Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset.

Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis

“…In addition to recommendations regarding THV sizing, AI could also help guide intra-procedural operations during TAVR. For example, the advent of real-time segmentation of THV and delivery systems based on intra-procedural angiography provides broader views that greatly reduce operational difficulties [ 107 , 112 ]. Furthermore, procedural techniques such as implantation depth, which is related to peri-operative conduction abnormality [ 113 ], could become more refined using patient-specific computer simulation (PSCS) [ 24 , 25 ].…”

Automatic Assessment of Transcatheter Aortic Valve Implantation Results on Four-Dimensional Computed Tomography Images Using Artificial Intelligence

“…Therefore, the automation of the interpretation of TAVI images is concerning and could significantly help the clinical practice, minimizing the effort per analysis. Furthermore, the employment of computers and artificial intelligence techniques for image interpretation would provide quantitative information that can be insightful for TAVI assessment [13].…”