Abstract 10961: Integrated Coronary Vascular Disease Diagnosis System Based on Two-Step Neural Network Algorithm Trained by Synthetic Model and Patient Biometric Features

Abstract

Introduction: Fractional flow reserve (FFR) is the golden standard that determines whether coronary artery disease (CAD) surgery is necessary or not. Invasive FFR uses pressure wire to obtain the pressure ratio of proximal and distal lesion. However, the burden from high cost and time due to invasive process makes it difficult to perform diagnosis. To overcome these limitations, FFR can be measured non-invasive through CFD analysis based on geometry models obtained from observational equipment such as coronary computer tomography (CT). However, obtaining this FFR value consumes considerable time and computer resources in the process of vessel shape extraction and CFD. In this study, we propose a non-invasive artificial intelligence-based FFR prediction technology. Methods & Results: The algorithms used in this paper were separated into two steps. The first algorithm is to compute flow features from geometric features trained by synthetic models and CFD analysis. In the second step, we trained classification algorithms and regression algorithms using the flow properties obtained from the first step and patient biometric information. Classification algorithms are used to determine whether to invasive operate, and regression algorithms are used to predict FFR values. The algorithms used are 6 machine learning and 2 neural networks. To improve the performance of the algorithms, outlier data were removed through the IQR method. Among the classification algorithms, random forest had the best accuracy of 80.1%, and in the regression algorithm, artistic neural network had the lowest mean absolute error of 0.04. Furthermore, we analyze the algorithm performance within the gray zone to prove that the algorithm can provide guidance in the case of uncertain situations. Conclusions: Algorithms learned in a two-step manner determined whether the lesion need a surgery or not and predicted the FFR value. In particular, we presented guidelines for building FFR prediction algorithms with good prediction performance.