Strain Curve Classification Using Supervised Machine Learning Algorithm with Physiologic Constraints

Ultrasound Med Biol. 2020 Sep;46(9):2424-2438. doi: 10.1016/j.ultrasmedbio.2020.03.002. Epub 2020 Jun 4.


Speckle tracking echocardiography (STE) enables quantification of myocardial deformation by a generation of spatiotemporal strain curves or time-strain curves (TSCs). Currently, only assessment of peak global longitudinal strain is employed in clinical practice because of the uncertainty in the accuracy of STE. We describe a supervised machine learning, physiologically constrained, fully automatic algorithm, trained with labeled data, for classification of TSCs into physiologic or artifactual classes. The data set of 415 healthy patients, with three cine loops per patient, corresponding to the three standard 2-D longitudinal views, was processed using a previously published, in-house STE software termed K-SAD. We report an accuracy of 86.4% for classifying TSCs as physiologic, artifactual and undetermined curves. The positive predictive value for a physiologic strain curve is 89%. This is as a necessary step for a similar separation of pathologic conditions, to allow full utilization of the temporal information concealed in layer-specific segmental TSCs.

Keywords: Echocardiography; Machine learning; Myocardial strain; Time–strain curves; Tracking quality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Echocardiography / methods*
  • Electrophysiologic Techniques, Cardiac
  • Female
  • Heart / physiology*
  • Humans
  • Male
  • Supervised Machine Learning*