Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Although echo is a very valuable tool, the image analysis is a time-consuming, resource-demanding process, and is susceptible to interreader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and interreader variability in the field of medical imaging. In the present study, we developed a fully automated tool, mouse-echocardiography neural net (MENN), for the analysis of both long-axis brightness (B)-mode and short-axis motion (M)-mode images of left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85-0.99) and good-to-excellent agreement between automated and manual analyses. Further interreader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of interreader variability in manual analysis.NEW & NOTEWORTHY Echocardiography is commonly used in preclinical research to evaluate cardiac structure and function. Despite the broad applications across therapeutic areas, the analysis of echo data is laborious and susceptible to interreader variability. In this study, we developed a fully automated mouse-echocardiography neural net (MENN). Cardiac measurements from MENN showed excellent correlations with manual analysis. Furthermore, the use of MENN leads to >92% reduction in analysis time and potentially eliminates the interobserver variability issue.
Keywords: automated image analysis; deep learning; echocardiography; heart failure; preclinical imaging.