When Diagnosis Is Made By Playing Shuffled Cards: A Non-Invasive, Real-Time, Adaptive Method to Functionally Evaluate Coronary Artery Based on Coronary Angiography
Dai, Y.; Zhu, P.; Xue, B.; Ling, Y.; Shi, X.; Geng, L.; Xie, Y.; Zhang, Q.; Liu, J.
Show abstract
AimsThe 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 ResultsWe 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). ConclusionWe 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. DisclosureThe Authors declare that there is no conflict of interest.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Detecting Heart Failure using novel bio-signals and a Knowledge Enhanced Neural Network 93%
- Biomechanical stress analysis of Type-A aortic dissection at pre-dissection, post-dissection, and post-repair states 92%
- Radiomics Analysis Using Stability Selection Supervised Principal Component Analysis for Right-censored Survival Data 92%
Similar papers in this journal
- AngioNet: A Convolutional Neural Network for Vessel Segmentation in X-ray Angiography 95%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 95%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 94%
Similar papers in this journal
- A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning 96%
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 94%
- Self-Supervised Electrocardiograph De-noising 94%
Similar papers in this journal
- Development and validation of a deep learning algorithm using fundus photographs to predict 10-year risk of ischemic cardiovascular diseases among Chinese population 93%
- Extensive adaptive immune response of AAVs and Cas proteins in non-human primates 86%
- X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis 84%
Similar papers in this journal
- Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography 96%
- Reactivation of atrium genes is a primer for heart infarction or regeneration 92%
- Analysis of vascular architecture and parenchymal damage generated by reduced blood perfusion in decellularized porcine kidneys using a gray level co-occurrence matrix 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.