154 Can we improve the accuracy and reproducibility of left ventricular ejection fraction from 2D echocardiography using artificial intelligence? A validation against cardiac magnetic resonance
Authors: Popoff A , Langet H , Piro P , Ropert C , Allain P , Gauriau R , Pizaine G , De Craene M , Gerber B , Saloux E 
Published: January 2020

Abstract

Funding Acknowledgements
Philips
BACKGROUND
Accurate and reproducible echocardiographic measurements are paramount for objective assessment and follow-up of the cardiac function. However, manual contouring – e.g., for determining left ventricular (LV) volumes and ejection fraction (EF) – is limited by image quality and operator experience. Meanwhile, despite the wider availability of (semi-)automated tools, strong multimodal validation is still lacking for their widespread and safe use in the clinical routine.
PURPOSE
To evaluate the accuracy and reproducibility of an Artificial Intelligence (AI)-based semi-automated tool to compute LV volumes and EF, in comparison with manual contouring, using cardiac magnetic resonance (cMR) as reference.
METHODS
Manual and AI measurements from echocardiography were compared to measurements from cMR in a retrospective two-centre study. One hundred fourteen patients in sinus rhythm were included; among those, 85 had abnormal LV function (56 dilated and 29 hypertrophic). Three successive cardiac cycles were available for apical 4- and 2-chamber views. Two senior (A1 and B1) and one junior (A2) cardiologists contoured the ED and ES endocardial borders in the cardiac cycle of their choice, while blinded to quantitative outcomes. For AI analysis, a deep convolutional neural networks model was used to segment the LV cavity on the frames selected by the three observers. This model was trained using ED and ES manual contouring from senior cardiologist A1 on an independent single-centre dataset that consisted of 700 apical 4- and 2-chamber views. The same biplane Simpson’s method was used to compute all LV volumes and EF.
RESULTS
Despite challenging image quality (poor: 6%; fair: 33%; high: 61%, as rated by observers), the majority of the AI segmentations were deemed acceptable (75% in total; 80% for images of high quality). Overall, inter-observer agreement was better by AI than by manual contouring (ICC = 0.99 vs. 0.89, 1.00 vs. 0.95 and 0.95 vs. 0.89 for LVED, LVES and LVEF respectively, all p &lt; 0.001). For LVED and LVES, agreement vs. cMR was higher by AI (80.95 ± 39.09; -46.42 ± 38.29) than by manual contouring for junior observer A2 (-81.47 ± 43; -51.88 ± 40.43), although still lower than by manual contouring for the best senior observer (-54.71 ± 31.44; -32.75 ± 32.80), see upper part in figure below. LVEF bias was reduced near to zero by AI, with slightly higher variability than by manual contouring ([-0.91; -0.05] ± [8.47; 10.17] vs. [-0.19; 5.44] ± [7.75; 8.79]), see lower part in figure below.

CONCLUSION
The AI model generalized well to different sites, observers and image quality. Compared to manual contouring, LV volumes and EF by AI showed comparable or improved accuracy and higher reproducibility. These findings demonstrate the value of AI-based tools, with potential for full automation, for objective assessment and follow-up of the cardiac function.
Abstract 154 Figure.