Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data

Hyeong Jun Lee, Young Woo Kim, Jun Hong Kim, Yong Joon Lee, Jinseok Moon, Peter Jeong, Joonhee Jeong, Jung Sun Kim, Joon Sang Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number106827
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - 2022 Jun

Bibliographical note

Funding Information:
Studies on image extraction, CFD analysis, and algorithm integration were supported by the Korea Medical Device Development Fund grant funded by the Korea government (The Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (KMDF_PR_20200901_0104, 9991007267). Also, this work was partially supported by E8IGHT© for the algorithm optimization.

Publisher Copyright:
© 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Health Informatics
  • Computer Science Applications


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