PO-04-212 BRUGADA EKG CLASSIFICATION WITH SELF-SUPERVISED VICREG PERTAINING: A NOVEL ARTIFICIAL INTELLIGENCE ARCHITECTURE FOR RARE ARRHYTHMIA

Abstract

Brugada Syndrome (BrS) is a rare inherited arrhythmia syndrome with an estimated prevalence of 2-5 per 10,000. Despite its low prevalence, it is responsible for an estimated 4% to 12% of all sudden deaths and in patients with structurally normal hearts. Because of the resulting limited number of available BrS EKG examples, Convoluted Neural Networks (CNNs), which require a large labeled dataset for training, are limited in their ability to automate the detection of BrS. To develop an Artificial Intelligence (AI) model for classifying BrS in the surface EKG by applying a novel self-supervised pretraining architecture on unrelated, unlabeled EKGs, compensating for the limited number of BrS examples. We pretrained a convolutional neural network on randomly selected 3,500,000 EKGs from adult NYU patients utilizing VICReg (Variance-Invariance-Covariance Regularization), a novel regularization architecture for self-supervised training allowing the model to learn embedding (vectors) for each EKG that are informative and unique (Figure 1). We then fine tuned the original model on a set containing 200 adult NYU patients with a MUSE (GE, USA) diagnosis of BrS not included in the pretraining phase, and evaluated the fine-tuned model for binary classification of Brugada. We used a modification of a multilayered convolutional neural network architecture similar to ConvNext, with a Softmax activation function for classification. Of the 200 EKGs with a MUSE label of BrS, 114 (57%) were confirmed as BrS by an expert Electrophysiologist and blended with a cohort of randomly selected 200 EKGs with non – BrS label. The combined cohort was divided into 80% training (with 20% validation) and 20% test sub-sets. The CNN classified BrS with an AUC of 0.953 (sensitivity 0.87, specificity 0.87), outperforming all previously described supervised learning models trained on larger datasets. We developed a novel deep learning model using VICReg architecture for self-supervised pretraining. We demonstrate the classification of BrS EKGs with high performance achieved with a remarkably small labeled dataset. Supervised pretraining with VICReg may be a useful architecture for improving model classification performance on rare conditions with limited availability of labeled data.