To main content

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning

Abstract

Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ultrasound acquisition errors such as apical foreshortening. In this work, a real-time and fully automated EF measurement and foreshortening detection method is proposed. The method uses several deep learning components, such as view classification, cardiac cycle timing, segmentation and landmark extraction, to measure the amount of foreshortening, LV volume, and EF. A data set of 500 patients from an outpatient clinic was used to train the deep neural networks, while a separate data set of 100 patients from another clinic was used for evaluation, where LV volume and EF were measured by an expert using clinical protocols and software. A quantitative analysis using 3-D ultrasound showed that EF is considerably affected by apical foreshortening, and that the proposed method can detect and quantify the amount of apical foreshortening. The bias and standard deviation of the automatic EF measurements were -3.6 ± 8.1%, while the mean absolute difference was measured at 7.2% which are all within the interobserver variability and comparable with related studies. The proposed real-time pipeline allows for a continuous acquisition and measurement workflow without user interaction, and has the potential to significantly reduce the time spent on the analysis and measurement error due to foreshortening, while providing quantitative volume measurements in the everyday echo lab.
Read the publication

Category

Academic article

Language

English

Author(s)

  • Erik Smistad
  • Andreas Østvik
  • Ivar Mjåland Salte
  • Daniela Melichova
  • Thuy Mi Nguyen
  • Kristina Haugaa
  • Harald Brunvand
  • Thor Edvardsen
  • Sarah Leclerc
  • Olivier Bernard
  • Bjørnar Grenne
  • Lasse Løvstakken

Affiliation

  • SINTEF Digital / Health Research
  • University Claude Bernard (Lyon I)
  • University of Oslo
  • St. Olavs Hospital, Trondheim University Hospital
  • Norwegian University of Science and Technology
  • Sørlandet Hospital Trust
  • Oslo University Hospital

Year

2020

Published in

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control

ISSN

0885-3010

Volume

67

Issue

12

Page(s)

2595 - 2604

View this publication at Norwegian Research Information Repository