Abstract 13558: Opening the Black Box of Artificial Intelligence: Visualization of Detecting Heart Failure Subtypes Using Electrocardiography

Abstract

Objectives: To evaluate and visualize how artificial intelligence (AI) recognizes electrocardiography (ECG) characteristics of each heart failure (HF) subtype. We developed a visually explainable and interpretable AI-ECG algorithm for various HF subtypes to meet this need. This study proposes the ShapeExplainer (SE) to interpret models. Methods: From 33,920 patients at two hospitals, we trained a convolutional neural network to identify patients with HF subtypes using ECG and clinical diagnosis. HF subtypes include heart failure with reduced ejection fraction (HFrEF), heart failure with mid-range ejection fraction (HFmrEF, and heart failure with preserved ejection fraction (HFpEF) ( Figure a ). To interprete each HF classifier model, SE, which converts non-HF group data to be recognized as each HF subtype by each classifier, was trained. The converted fake ECG shows what the change is for the classifier to recognize as each HF subtype. SE from the non-HF group of the external validation set shows the result by comparing the shape before and after transformation. Results: When tested on an independent set of 6,784 patients, the network model yielded values for the area under the curves of HFrEF of 0.945, HFmrEF of 0.884, and HFpEF of 0.859, respectively. The results of each classifier were visualized by overlapping the original and newly generated ECGs. Comparing each subtype and its fake ECG, there was no clear trend of electrocardiographic features. However, fake ECG showed a tendency of qrs amplitude as it progressed to non-HF, HFpEF, HFmrEF, and HFrEF, especially in T wave or ST-change ( Figure b ). Conclusions: The proposed fake ECG generating method is a new approach for interpreting the AI-ECG model, helping physicians understand how AI recognizes HF subtypes.