Back

Machine learning-based survival models integrating quantitative flow ratio to predict in-stent restenosis after percutaneous coronary intervention

29 August 2026 (09:15 - 10:00)
Organised by: Logo
Congress Presentation Part of: Myocardial infarction: artificial intelligence prediction and prognosis Machine Learning ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

Photo
Doctor Tong Liu

2nd Hospital of Tianjin Medical University, Tianjin (China)

6 More presentations in this session

Presentation thumbnail
AI-ECG prediction of long-term adverse cardiovascular outcomes in post myocardial infarction patients
Mr L. Lovatelli (GB)
Abstract Slides Video
Presentation thumbnail
Chest pain virtual ward (CPVW): a prospective analysis of a novel care model for low-risk acute coronary syndrome
Doctor T. Chakraborty (London, GB)
Abstract Slides Video
Presentation thumbnail
The joint impact of frailty and premature myocardial infarction on one-year outcomes：biological vulnerability versus chronological age
Doctor R. Guo (Tianjin, CN)
Presentation thumbnail
Prognostic significance of explainable machine learning-defined type 2 myocardial infarction: unmasking a high-risk phenotype in multicenter data
Professor K. Chen (Tianjin, CN)
Abstract Slides
Presentation thumbnail
Impact of intra-patient variability of artificial intelligence and machine learning models on the diagnosis of myocardial infarction
Mr W. Zeng (Basel, CH)
Abstract Slides Video

Access the full session

Session thumbnail
Myocardial infarction: artificial intelligence prediction and prognosis

The Event

Event poster

ESC Congress 2026

29 August 2026 09:15 CET
You may be interested in
thumbnail
Congress Presentation
Machine learning for predicting fractional flow reserve based on optical coherence tomography in intermediate coronary stenosis
30 August 2020
thumbnail
Congress Presentation
What drives success in models predicting heart failure outcome?
28 August 2020
thumbnail
Congress Presentation
Angiography-derived QFR and RWS predict in-stent restenosis after PCI: a retrospective cohort study
31 August 2025
thumbnail
Congress Presentation
The specific characteristics and independent predictors of no-reflow phenomenon, development of a clinically-adaptable risk estimation system
31 August 2019

ESC 365 is supported by

novartis Explore sponsored resources
novo-nordisk Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources
novo-nordisk Explore sponsored resources