Prediction of FFR from IVUS Images Using Machine Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-17

AUTHORS

Geena Kim , June-Goo Lee , Soo-Jin Kang , Paul Ngyuen , Do-Yoon Kang , Pil Hyung Lee , Jung-Min Ahn , Duk-Woo Park , Seung-Whan Lee , Young-Hak Kim , Cheol Whan Lee , Seong-Wook Park , Seung-Jung Park

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. More... »

PAGES

73-81

References to SciGraph publications

  • 2001-10. Random Forests in MACHINE LEARNING
  • 2015-12. ImageNet Large Scale Visual Recognition Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

    ISBN

    978-3-030-01363-9
    978-3-030-01364-6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-01364-6_9

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    http://dx.doi.org/10.1007/978-3-030-01364-6_9

    DIMENSIONS

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