540 Assessment of left ventricular ejection fraction from echocardiographic images using machine learning algorithm

Abstract

Abstract Background Precise and reliable echocardiographic assessment of LVEF is needed for clinical decision-making. LVEF is currently determined through an observer dependent process that requires manual tracing. To remove this manual tracing step, which is both time-consuming and user dependent, automatic computer aided diagnosis systems may be useful in the clinical setting. Purpose The aim of this study was to evaluate whether a 3-dimensional convolutional neural networks (3DCNN) could estimate left ventricular ejection fraction (LVEF) and differentiate types of heart failure (preserved EF/reduced EF) using conventional 2-dimensional echocardiographic images. Methods We developed a deep learning model to automatically estimate LVEF from echocardiographic data. The 3DCNN model was trained on a dataset of 340 patients. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the parasternal and apical views were manually selected; then, 10 sequential frames were extracted from each 1 beat and; finally, each frame was pre-processed to fit the learning model. Each patient has 2 views, resulting in a total of 6,800 images. Reference LVEF measurement was calculated by two highly experienced readers in each case. Results A good correlation was found between estimated LVEF based on apical 2 and 4 chamber views and reference LVEF (r =0.88, p <0.001) (Figure). For classification of heart failure types based on LVEF (LVEF ≥50% or <50%), the area under the receiver-operating characteristic curve by the 3DCNN algorithm was over 0.95. Conclusions The 3DCNN can be applied to estimate and classify the LVEF in the clinical setting. Furthermore, this work will serve as a driver for future research using million image databases. Abstract 540 Figure.