RESUMO
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.
RESUMO
As deep learning plays an increasing role in making medical decisions, explainability is playing an increasing role in satisfying regulatory requirements and facilitating trust and transparency in deep learning approaches. In cardiac imaging, the task of accurately assessing left-ventricular function is crucial for evaluating patient risk, diagnosing cardiovascular disease, and clinical decision making. Previous video based methods to predict ejection fraction yield high accuracy but at the expense of explainability and did not utilize the standard clinical workflow. More explainable methods that match the clinical workflow, using 2D semantic segmentation, have been explored but found to have lower accuracy. To simultaneously increase accuracy and utilize an approach that matches the standard clinical workflow, we propose a frame-by-frame 3D depth-map approach that is both accurate (mean absolute error of 6.5%) and explainable, utilizing the conventional clinical workflow with method of discs evaluation of left ventricular volume. This method is more reproducible than human evaluation and generates volume predictions that can be interpreted by clinicians and provide the opportunity to intervene and adjust the deep learning prediction.
