Deep Learning-Derived Myocardial Strain.
JACC Cardiovasc Imaging
; 17(7): 715-725, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38551533
ABSTRACT
BACKGROUND:
Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses.OBJECTIVES:
This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations.METHODS:
Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy.RESULTS:
In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC] 0.56, P = 0.002) with a bias of -3.31% (limits of agreement -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis ICC was 0.64 (95% CI 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS.CONCLUSIONS:
The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Echocardiography
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Image Interpretation, Computer-Assisted
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Observer Variation
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Predictive Value of Tests
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Ventricular Function, Left
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Deep Learning
Limits:
Aged
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Female
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Humans
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Male
/
Middle aged
Language:
En
Journal:
JACC Cardiovasc Imaging
Journal subject:
ANGIOLOGIA
/
CARDIOLOGIA
/
DIAGNOSTICO POR IMAGEM
Year:
2024
Type:
Article