Key result
Optimized multilayer perceptron regressor outperforms other tested machine learning models in estimating gray zone FFR.
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Why the study?
Artificial intelligence algorithms for coronary diagnosis have limitations in data quantity and quality, and previous studies mostly used vessel images without incorporating flow and biometric features, requiring optimization especially within the gray zone for FFR estimation.
An optimized multilayer perceptron regressor algorithm incorporating flow and biometric features improves FFR estimation accuracy in the diagnostic gray zone.
BACKGROUND: Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. OBJECTIVES: The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. METHOD: The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. CONCLUSION: Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization.
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Lee et al. (2022) studied Coronary artery disease. Artificial intelligence-based coronary vascular diagnosis system vs. Other machine learning models was evaluated on Gray zone accuracy of FFR estimation. An optimized multilayer perceptron regressor demonstrated the highest accuracy for estimating fractional flow reserve in the gray zone among eight tested machine learning and neural network models.
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