Apical Sparing Strain Imaging In Patients With Hypertensive Emergency: A Study Of Patients Characteristics And Prognostic Value
HFSA ePoster Library. Alsheikhly K. 09/10/21; 343515; 278
Kootaybah Alsheikhly
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Abstract
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Background: Echocardiography is the test of choice to assess cardiac function to diagnose and manage heart disease. However, manual interpretation of the echocardiogram is time-consuming and subject to human error. Therefore, we demonstrate a fully automated deep learning workflow to classify, segment, and annotate 2D videos and Doppler modalities.
Methods: The workflow was developed using a total of 20,828 annotated 2D and Doppler modalities images from 1,145 studies (N) from a heart failure research platform in Singapore. We validated the workflow against human measurements in a dataset from Canada (N=1,029), a 'real-world' dataset from Taiwan (N=31,241), the US-based EchoNet-Dynamic dataset (N=10,030); and in an independent prospective Singapore dataset (N=29 to 66) with repeated human expert measurements.
Results: In the test set, the automated workflow classified 2D videos and Doppler modalities with accuracies ranging from 0.91 to 0.99. The left ventricle and left atrium segmentation were accurate, with a mean Dice similarity coefficient >93% for all. In the external datasets, automated measurements showed good agreement with locally measured values, with a mean absolute error range of 13-25ml for left ventricular volumes, 6-10% for left ventricular ejection fraction, and 1.8-2.0 for E/e'; and reliably classified systolic dysfunction (LVEF<40%, area under the curve [AUC] range 0.90-0.92, Figure A) and diastolic dysfunction (E/e' ≥13, AUC range 0.91-0.91, Figure B). Independent prospective evaluation confirmed similar or less variance of automated compared to human expert measurements.
Conclusions: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy compared with human measurements.
Methods: The workflow was developed using a total of 20,828 annotated 2D and Doppler modalities images from 1,145 studies (N) from a heart failure research platform in Singapore. We validated the workflow against human measurements in a dataset from Canada (N=1,029), a 'real-world' dataset from Taiwan (N=31,241), the US-based EchoNet-Dynamic dataset (N=10,030); and in an independent prospective Singapore dataset (N=29 to 66) with repeated human expert measurements.
Results: In the test set, the automated workflow classified 2D videos and Doppler modalities with accuracies ranging from 0.91 to 0.99. The left ventricle and left atrium segmentation were accurate, with a mean Dice similarity coefficient >93% for all. In the external datasets, automated measurements showed good agreement with locally measured values, with a mean absolute error range of 13-25ml for left ventricular volumes, 6-10% for left ventricular ejection fraction, and 1.8-2.0 for E/e'; and reliably classified systolic dysfunction (LVEF<40%, area under the curve [AUC] range 0.90-0.92, Figure A) and diastolic dysfunction (E/e' ≥13, AUC range 0.91-0.91, Figure B). Independent prospective evaluation confirmed similar or less variance of automated compared to human expert measurements.
Conclusions: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy compared with human measurements.
Background: Echocardiography is the test of choice to assess cardiac function to diagnose and manage heart disease. However, manual interpretation of the echocardiogram is time-consuming and subject to human error. Therefore, we demonstrate a fully automated deep learning workflow to classify, segment, and annotate 2D videos and Doppler modalities.
Methods: The workflow was developed using a total of 20,828 annotated 2D and Doppler modalities images from 1,145 studies (N) from a heart failure research platform in Singapore. We validated the workflow against human measurements in a dataset from Canada (N=1,029), a 'real-world' dataset from Taiwan (N=31,241), the US-based EchoNet-Dynamic dataset (N=10,030); and in an independent prospective Singapore dataset (N=29 to 66) with repeated human expert measurements.
Results: In the test set, the automated workflow classified 2D videos and Doppler modalities with accuracies ranging from 0.91 to 0.99. The left ventricle and left atrium segmentation were accurate, with a mean Dice similarity coefficient >93% for all. In the external datasets, automated measurements showed good agreement with locally measured values, with a mean absolute error range of 13-25ml for left ventricular volumes, 6-10% for left ventricular ejection fraction, and 1.8-2.0 for E/e'; and reliably classified systolic dysfunction (LVEF<40%, area under the curve [AUC] range 0.90-0.92, Figure A) and diastolic dysfunction (E/e' ≥13, AUC range 0.91-0.91, Figure B). Independent prospective evaluation confirmed similar or less variance of automated compared to human expert measurements.
Conclusions: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy compared with human measurements.
Methods: The workflow was developed using a total of 20,828 annotated 2D and Doppler modalities images from 1,145 studies (N) from a heart failure research platform in Singapore. We validated the workflow against human measurements in a dataset from Canada (N=1,029), a 'real-world' dataset from Taiwan (N=31,241), the US-based EchoNet-Dynamic dataset (N=10,030); and in an independent prospective Singapore dataset (N=29 to 66) with repeated human expert measurements.
Results: In the test set, the automated workflow classified 2D videos and Doppler modalities with accuracies ranging from 0.91 to 0.99. The left ventricle and left atrium segmentation were accurate, with a mean Dice similarity coefficient >93% for all. In the external datasets, automated measurements showed good agreement with locally measured values, with a mean absolute error range of 13-25ml for left ventricular volumes, 6-10% for left ventricular ejection fraction, and 1.8-2.0 for E/e'; and reliably classified systolic dysfunction (LVEF<40%, area under the curve [AUC] range 0.90-0.92, Figure A) and diastolic dysfunction (E/e' ≥13, AUC range 0.91-0.91, Figure B). Independent prospective evaluation confirmed similar or less variance of automated compared to human expert measurements.
Conclusions: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy compared with human measurements.
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