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A deep learning method accurately quantifies left ventricular (LV) function from cardiac MRI. Training with diverse data improved performance across multiple vendors and centers for automated LV analysis.

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Accurate quantification of left ventricular (LV) function is crucial for diagnosing and managing cardiovascular diseases.
  • Manual analysis of cardiac MRI is time-consuming and subject to inter-observer variability.
  • Deep learning offers potential for automated and standardized LV function assessment.

Purpose of the Study:

  • To develop a deep learning-based method for fully automated quantification of LV function from short-axis cine MR images.
  • To evaluate the performance of this method in a multivendor and multicenter setting.

Main Methods:

  • Retrospective analysis of cine MRI data from three vendors and four centers (2008-2016).
  • Training of three U-NET architecture convolutional neural networks (CNNs) on datasets with increasing variability (CNN1, CNN2, CNN3).
  • Independent testing on a multivendor, multicenter dataset (196 patients); performance evaluated against manual annotations for LV detection, segmentation, and functional parameter accuracy.

Main Results:

  • CNN3, trained on the most diverse dataset, achieved the highest performance on independent testing.
  • CNN3 demonstrated significantly lower average perpendicular distance (1.1 mm ± 0.3) compared to CNN1 and CNN2 (P < .05).
  • LV functional parameters derived from CNN3 showed high correlation (r² ≥ 0.98) and agreement with expert analysis across different vendors and centers.

Conclusions:

  • A deep learning method trained on a highly variable dataset enables fully automated and accurate cine MRI analysis.
  • The developed method demonstrates robust performance in multivendor and multicenter settings, reducing reliance on manual analysis.
  • This approach holds promise for improving the efficiency and consistency of LV function assessment in clinical practice.