A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features

Ali Ahmad; Zahi Ibrahim; Georges Sakr; Abdallah El-Bizri; Lara Masri; Imad H Elhajj; Nehme El-Hachem; Hussain Isma'eel

doi:10.21037/cdt-20-471

A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features

Cardiovasc Diagn Ther. 2020 Aug;10(4):859-868. doi: 10.21037/cdt-20-471.

Authors

Ali Ahmad^{1

2}, Zahi Ibrahim¹, Georges Sakr³, Abdallah El-Bizri⁴, Lara Masri¹, Imad H Elhajj⁵, Nehme El-Hachem⁵, Hussain Isma'eel^{1

4}

Affiliations

¹ Vascular Medicine Program, Division of Cardiology, American University of Beirut, Beirut, Lebanon.
² Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
³ Department of Computer Engineering, St Joseph University of Beirut, Beirut, Lebanon.
⁴ Department of Internal Medicine, American University of Beirut, Beirut, Lebanon.
⁵ Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.

Abstract

Background: Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard.

Methods: We retrospectively identified patients who underwent a 2D-ECHO and a CMR within 3 months of each other at our institution (American University of Beirut Medical Center). We extracted three parameters (TAPSE, S' and FAC_RV) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems.

Results: One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62.

Conclusions: In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.

Keywords: 2D-ECHO; CMR; RV function; machine learning.