When Diagnosis Is Made By Playing “Shuffled Cards”: A Non-Invasive, Real-Time, Adaptive Method to Functionally Evaluate Coronary Artery Based on Coronary Angiography

Abstract

Aims The great value concealed in the sequence of coronary angiography frames is not discovered in the world. We discovered and demonstrated the Sequence Value concealed in the coronary angiography and proposed DZL (Disarranged Zone Learning) to realize the sequence value in functional evaluation of coronary artery disease. Furthermore, we automated the DZL using a deep learning model to release huge medical resources. Methods and Results We gave a novel definition of TIMI flow grade using the term temporal and spatial coupling tightness (TSCT) of the antegrade contrast agent. We used the TSCT to model the myocardial ischemia in a functional perspective and used the PCI conduction after CAG as the outcome event of myocardial ischemia. We proposed a novel method (Disarranged Zone Learning) to measure TSCT and we designed an experiment to validate its effectiveness. We further automated the novel method using an unsupervised deep learning model. The prediction accuracy of the model was applied as a proxy of myocardial ischemia. We further proposed Difference DZL to quantify the functional capability of any specific vessel segment. DZL overall AUC reaches 0.92. DZL automation reveals an AUC of 0.84 (95%CI, 0.81-0.87). Conclusion We unprecedentedly discovered the Sequence Value concealed in coronary angiography. We then proposed a novel method termed DZL to functionally evaluate the coronary artery in a non-invasive, real-time and adaptive manner.