Myocardial ischemic classification using a knowledge-guided polar transformer in two-dimensional echocardiography

Abstract

Myocardial ischemia, characterized by inadequate blood supply to the heart muscles, is critical to cardiovascular diseases. Timely and accurate identification of ischemic segments is essential for prompt intervention and patient care. This study developed a Transformer-based model to identify myocardial ischemia in left ventricle short-axis (LVSA) two-dimensional echocardiography (2DE) images where a novel Knowledge-Guided Polar Transformer (KGPT) was proposed that integrated the unique characteristics of 2DE images with the prior clinical knowledge. 305 patients (aged 57.6 ± 8.8 years) were selected and underwent transthoracic echocardiography within 1–3 days prior to invasive coronary angiography (ICA). With ICA and quantitative flow ratio as the gold standard of myocardial ischemia, the KGPT model was trained to classify the LVSA 2DE images as ischemia or non-ischemia by capturing spatial features in a radial orientation. Its performance was evaluated with five-fold cross-validation and receiver operating characteristic curve (ROC) analysis. It achieved an area under ROC (AUC) of 0.8326 ± 0.0906, with an accuracy of 79.50 ± 5.40 %, precision of 79.07 ± 6.70 %, recall of 80.79 ± 7.87 %, and F1 score of 78.43 ± 6.56 %. In comparison, the original Swin-Transformer model produced an AUC of 0.7011 ± 0.0334, accuracy of 70.20 ± 1.04 %, precision of 68.58 ± 3.12 %, recall of 63.21 ± 3.60 %, and F1 score of 63.13 ± 3.78 %. The differences were statistically significant (P < 0.05). The KGPT also demonstrated significantly superior performance to radiologists. It effectively classifies ischemic regions in 2DE images, presenting a promising tool for diagnosing myocardial ischemia. The integration of clinical knowledge with Transformer enhances the accuracy and reliability of ischemia classification, potentially revolutionizing the diagnosis and monitoring of myocardial ischemic diseases.