A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction

TL;DR

This paper introduces a novel multimodal deep learning framework combining echocardiography and CMR data to improve the accuracy of predicting patient response to cardiac resynchronisation therapy, demonstrating significant performance gains.

Contribution

It is the first to develop a multimodal deep learning approach for CRT response prediction, integrating two imaging modalities to enhance prediction accuracy.

Findings

Multimodal classifier achieves 77.38% accuracy.

Significant improvement over echocardiography-only models.

Comparable to current state-of-the-art methods.

Abstract

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.