Abstract 4142793: Development of an Angiography-Based Multimodal AI Model for Predicting the risk of In-Stent Restenosis

Background: Despite advances in Second-generation drug-eluting stents (DES), 5-10% of patients still experience in-stent restenosis (ISR) after percutaneous coronary intervention (PCI), which generates significant financial burden and elevates the risk of acute coronary syndrome (ACS) and rehospitalization. Thus, early identification of patients at high risk for ISR is crucial for guiding clinical stratification and early intervention. Aims: To develop and validate a multimodal artificial intelligence (AI) model based on coronary angiography images for predicting ISR risk in patients post-DES implantation. Methods: To establish an accurate predictive model, our approach begins with the pre-training on 100,000 angiographic images to enhance the model’s capability in recognizing image features. Subsequently, we employ the DenseNet architecture as the primary deep learning model, incorporating angiographic images from 2,000 cases of DES-treated de novo lesions—1,000 from patients who did not experience ISR within two years and 1,000 from those who did. A multivariate logistic regression analysis, including radiomic features, clinical baselines, and functional information, constructs the predictive model. Additionally, a separate prospective cohort of 300 cases was assembled for validation to simulate real-world application and to verify the model's reliability and accuracy. Results: Our study successfully developed an AI prediction model for ISR, utilizing a large cohort of coronary angiography images, which effectively predicts ISR with high accuracy. Leveraging the DenseNet architecture and finely tuned machine learning algorithms, the model achieved a sensitivity and specificity of 90% in the validation cohort. The ROC curve from the test phase demonstrated an AUC above 0.90, underscoring the model’s exceptional diagnostic capabilities. Furthermore, the implementation of this model in a prospective cohort confirmed its reliability and practical utility in real-world clinical settings. Conclusions: This study introduces the first multimodal AI model using angiographic imaging to predict ISR. By demonstrating high diagnostic accuracy and reliability in real-world settings, this model serves as an essential tool for early ISR detection and intervention, ultimately helping to reduce the incidence of major adverse cardiac events (MACEs) and mortality.