# Deep Learning‐Based Prediction Model for Cardiac Resynchronization Therapy Responders Using Electrocardiogram Data

## TL;DR

This study uses deep learning models on ECG data to predict which patients will respond to cardiac resynchronization therapy, aiming to improve treatment outcomes.

## Contribution

The study introduces deep learning models, including a self-supervised learning enhanced ResNet-18, for predicting CRT response using preimplantation ECG data.

## Key findings

- The SSL+ResNet-18 model showed the most stable performance with 78.5% accuracy and 81.3% positive predictive value.
- Grad-CAM analysis revealed that the model focused on different ECG leads, indicating diverse electrical features for CRT response prediction.

## Abstract

Cardiac resynchronization therapy (CRT) is an established treatment for advanced heart failure, but approximately 30% of patients fail to respond. This study aimed to develop and evaluate deep learning models using preimplantation electrocardiogram (ECG) data to predict CRT response.

We conducted a retrospective analysis of 285 patients who underwent CRT implantations and completed a 6‐month follow‐up. Responders were defined as those exhibiting ≥ 15% left ventricular end‐systolic volume reduction. Three models were developed: ResNet‐18 model trained on ECG images, self‐supervised learning (SSL) enhanced ResNet‐18 model, and LightGBM model trained on time‐series ECG data. Model performance was evaluated using accuracy, positive predictive value (PPV), and negative predictive value (NPV), averaged across 10 random seeds. Model interpretability using Gradient‐weighted Class Activation Mapping (Grad‐CAM) was performed on 36 responder cases.

The SSL + ResNet‐18 model demonstrated the most stable performance (accuracy 78.5% ± 5.5%) and PPV of 81.3%. The ResNet‐18 model achieved the highest PPV of 84.2% but had lower accuracy (74.1%) and larger variability. The LightGBM model exhibited the highest accuracy (79.4%) but the lowest PPV at 72.8%. Grad‐CAM showed that precordial leads were highlighted in 13 cases (36.1%), limb leads in 16 (44.4%), and both regions in 7 (19.4%), indicating heterogeneity in the model's focus and potential diversity in the electrical features contributing to CRT response prediction.

AI models using preimplantation ECG data, particularly those based on image inputs, can effectively predict CRT responders. This approach may enhance patient selection and support personalized therapy strategies in CRT management.