Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers - 03/07/24

Doi : 10.1016/j.echo.2024.03.017

Jeremy A. Slivnick, MD ^a, Nils T. Gessert, PhD ^b, Juan I. Cotella, MD ^a, Lucas Oliveira, PhD ^b, Nicola Pezzotti, PhD ^b, Parastou Eslami, PhD ^b, Ali Sadeghi, PhD ^b, Simon Wehle, PhD ^b, David Prabhu, PhD ^b, Irina Waechter-Stehle, PhD ^b, Ashish M. Chaudhari, PhD ^b, Teodora Szasz, PhD ^b, Linda Lee, MD ^a, Marie Altenburg, MD ^a, Giancarlo Saldana, MD ^a, Michael Randazzo, MD ^a, Jeanne M. DeCara, MD ^a, Karima Addetia, MD ^a, Victor Mor-Avi, PhD ^a, Roberto M. Lang, MD ^a,^⁎
^a University of Chicago Medical Center, Chicago, Illinois
^b Philips Healthcare, Cambridge, Massachusetts

^∗Reprint requests: Roberto M. Lang, MD, University of Chicago Medicine, 5758 S. Maryland Ave., MC 9067, Rm 5509, Chicago, IL 60637.University of Chicago Medicine5758 S. Maryland Ave.MC 9067, Rm 5509ChicagoIL60637

Abstract

Background

Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography, current methods are prone to interobserver variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers.

Methods

We used 15,746 transthoracic echocardiography studies—including 25,529 apical videos—which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical transthoracic echocardiography reports. Within the test cohort, DL model accuracy was compared to 6 expert and 3 novice readers using F1 score evaluation, with the ground truth of RWMA defined by expert readers. Significance between the DL model and novices was assessed using the permutation test.

Results

Within the test cohort, the DL model accurately identified any RWMA with an area under the curve of 0.96 (0.92-0.98). The mean F1 scores of the experts and the DL model were numerically similar for 6 of 7 regions: anterior (86 vs 84), anterolateral (80 vs 74), inferolateral (83 vs 87), inferoseptal (86 vs 86), apical (88 vs 87), inferior (79 vs 81), and any RWMA (90 vs 94), respectively, while in the anteroseptal region, the F1 score of the DL model was lower than the experts (75 vs 89). Using F1 scores, the DL model outperformed both novices 1 (P = .002) and 2 (P = .02) for the detection of any RWMA.

Conclusions

Deep learning provides accurate detection of RWMA, which was comparable to experts and outperformed a majority of novices. Deep learning may improve the efficiency of RWMA assessment and serve as a teaching tool for novices.

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Graphical abstract

Central Illustration :

Illustration of key findings of the study. The AI model was trained and validated using apical 2-, 3-, and 4-chamber images to predict the ground truth of RWMA from the clinical echocardiography report. The model was subsequently tested using a reader study format. Here, the AI model demonstrated comparable accuracy to the ground truth of expert readers and outperformed a majority of novice readers for RWMA detection.

Central IllustrationIllustration of key findings of the study. The AI model was trained and validated using apical 2-, 3-, and 4-chamber images to predict the ground truth of RWMA from the clinical echocardiography report. The model was subsequently tested using a reader study format. Here, the AI model demonstrated comparable accuracy to the ground truth of expert readers and outperformed a majority of novice readers for RWMA detection.

Le texte complet de cet article est disponible en PDF.

Highlights

•	Currently, assessment of regional wall motion is prone to interobserver variability.
•	We developed a DL model for regional wall motion assessment.
•	The model demonstrated excellent accuracy, equivalent to that of expert readers.
•	The model outperformed the majority of the novice readers.
•	It may prove useful, as it rapidly highlights areas of concern.