Localizing Cardiac Dyssynchrony in M-mode Echocardiography with Attention Maps.

Cardiac Resynchronization Therapy (CRT) is a treatment aimed at restoring the electrical synchronization in patients with heart failure and intraventricular conduction delay. However, over 30% of patients do not respond to CRT. Septal Flash (SF), an abnormality characterized by a rapid inward-outward abnormal motion at early systole, has been linked to an improved response to CRT in patients with Left Bundle Branch Block (LBBB). In clinical practice, the detection of SF is usually performed manually through echocardiographic acquisitions, which is subjective and dependent on the operator’s experience. To address this issue, a deep classification model for automatic SF detection from 2D anatomical M-mode images has been proposed. Additionally, this work focuses on SF localization from gradient-based attention maps, which provide a visual explanation of the output prediction of the model. Two models based on convolutional neural networks (CNNs) were trained with original and cropped M-modes from 143 patients, and achieved an accuracy of 0.83 and 1.0 respectively, on 29 testing patients. The attention map visualization showed that in SF cases, the models effectively identified the discriminant regions, while in non-SF cases, the maps appeared more dispersed. Further research is necessary to quantitatively evaluate the attention map results.