Prediction of FFR from IVUS images using machine learning

Authors

Geena Kim, Regis University
June Goo Lee, Asan Institute for Life Sciences
Soo Jin Kang, University of Ulsan College of Medicine
Paul Ngyuen, Regis University
Do Yoon Kang, University of Ulsan College of Medicine
Pil Hyung Lee, University of Ulsan College of Medicine
Jung Min Ahn, University of Ulsan College of Medicine
Duk Woo Park, University of Ulsan College of Medicine
Seung Whan Lee, University of Ulsan College of Medicine
Young Hak Kim, University of Ulsan College of Medicine
Cheol Whan Lee, University of Ulsan College of Medicine
Seong Wook Park, University of Ulsan College of Medicine
Seung Jung Park, University of Ulsan College of Medicine

Document Type

Conference Proceeding

Publication Date

1-1-2018

Abstract

We present a machine learning approach for predicting fractional flow reserve (FFR) from intravscular ultrasound images (IVUS) in coronary arteries. IVUS images and FFR measurements were collected from 1744 patients and 1447 lumen and plaque segmentation masks were generated from 1447 IVUS images using an automatic segmentation model trained on separate 70 IVUS images and minor manual corrections. Using total 114 features from the masks and general patient informarion, we trained random forest (RF), extreme gradient boost (XGBoost) and artificial neural network (ANN) models for a binary classification of FFR-80 threshold (FFR < 0.8 v.s. FFR ≥ 0.8) for comparison. The ensembled XGBoost models evaluated in 290 unseen cases achieved 81% accuracy and 70% recall.

Recommended Citation

Kim, Geena; Lee, June Goo; Kang, Soo Jin; Ngyuen, Paul; Kang, Do Yoon; Lee, Pil Hyung; Ahn, Jung Min; Park, Duk Woo; Lee, Seung Whan; Kim, Young Hak; Lee, Cheol Whan; Park, Seong Wook; and Park, Seung Jung, "Prediction of FFR from IVUS images using machine learning" (2018). Regis University Faculty Publications (comprehensive list). 360.
https://epublications.regis.edu/facultypubs/360