Algorithm for Recognition of Left Atrial Appendage Boundaries in Echocardiographic Images

Introduction: The left atrial appendage )LAA( occlusion using a purpose-built device is a growing procedure. This study aimed to develop a computer-aided diagnostic system for the recognition of the LAA in echocardiographic images. Material and Methods: The three-dimensional (۳D) echocardiographic images of the LAA of ۲۶ patients successfully treated with an LAA occluder were used in this study. A total of ۲۰۸ ۳D derived two-dimensional images in the axial plane were derived from each ۳D dataset. Then, ۵۶۲ images in which the LAA boundaries were highly recognizable were selected for this study. The proposed convolutional neural network (CNN) in this study was based on open-source object identification and classification platform compiled under the You Only Look Once algorithm. Finally, ۴۱۹ and ۱۴۳ images were used for training and testing the algorithm, respectively. Results: Algorithm performance on the identification of the LAA region on a set of ۱۴۳ images was compared to that reported for the traced regions on the same images by an expert in echocardiography using an intersection over the union (IOU) algorithm. The algorithm was able to correctly identify the LAA region in all ۱۴۳ examined images with an average IOU of ۹۰.۷%.  Conclusion: The proposed image-based CNN algorithm in this study showed high accuracy in the recognition of the LAA boundaries in the echocardiographic images. The method can be used in the development of algorithms for the automated analysis of the area of the LAA used for device sizing and procedural planning in the LAA occlusion procedures.

Keywords:

Artificial intelligence , Atrial fibrillation , Computer Vision , Echocardiographic , Left Atrial Appendage