EasyPISA: Automatic Integrated PISA Measurements of Mitral Regurgitation From 2-D Color-Doppler Using Deep Learning.

OBJECTIVE

The proximal isovelocity surface area (PISA) method is a well-established approach for mitral regurgitation (MR) quantification. However, it exhibits high inter-observer variability and inaccuracies in cases of non-hemispherical flow convergence and non-holosystolic MR. To address this, we present EasyPISA, a framework for automated integrated PISA measurements taken directly from 2-D color-Doppler sequences.

METHODS

We trained convolutional neural networks (UNet/Attention UNet) on 1171 images from 196 recordings (54 patients) to detect and segment flow convergence zones in 2-D color-Doppler images. Different preprocessing schemes and model architectures were compared. Flow convergence surface areas were estimated, accounting for non-hemispherical convergence, and regurgitant volume (RVol) was computed by integrating the flow rate over time. EasyPISA was retrospectively applied to 26 MR patient examinations, comparing results with reference PISA RVol measurements, severity grades, and cMRI RVol measurements for 13 patients.

RESULTS

The UNet trained on duplex images achieved the best results (precision: 0.63, recall: 0.95, dice: 0.58, flow rate error: 10.4 ml/s). Mitigation of false-positive segmentation on the atrial side of the mitral valve was achieved through integration with a mitral valve segmentation network. The intraclass correlation coefficient was 0.83 between EasyPISA and PISA, and 0.66 between EasyPISA and cMRI. Relative standard deviations were 46% and 53%, respectively. Receiver operator characteristics demonstrated a mean area under the curve between 0.90 and 0.97 for EasyPISA RVol estimates and reference severity grades.

CONCLUSION

EasyPISA demonstrates promising results for fully automated integrated PISA measurements in MR, offering potential benefits in workload reduction and mitigating inter-observer variability in MR assessment.