{Reference Type}: Journal Article
{Title}: Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.
{Author}: Sawano S;Kodera S;Setoguchi N;Tanabe K;Kushida S;Kanda J;Saji M;Nanasato M;Maki H;Fujita H;Kato N;Watanabe H;Suzuki M;Takahashi M;Sawada N;Yamasaki M;Sato M;Katsushika S;Shinohara H;Takeda N;Fujiu K;Daimon M;Akazawa H;Morita H;Komuro I;
{Journal}: PLoS One
{Volume}: 19
{Issue}: 8
{Year}: 2024
{Factor}: 3.752
{DOI}: 10.1371/journal.pone.0307978
{Abstract}: The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.