Clinical Impact of Intravascular Ultrasound-Based Artificial Intelligence Technologies (INNOVATE-PCI)

Summary

This study is a prospective, multicenter study in the real practice to validate the diagnostic performances and clinical impact of coronary angiography \& intravascular ultrasound (IVUS)-based models developed by machine learning (ML).

Eligibility

Inclusion Criteria:

* Age 19 years or older
* Symptomatic angina patients with objective myocardial ischemia
* Patients with at least one major epicardial coronary artery that requires stent implantation
* Subject who signs with informed consent form

Patient exclusion Criteria:

* ST-segment elevation MI at admission
* Patients who underwent coronary artery bypass surgery or heart transplantation
* Left ventricular ejection fraction \<30%
* Cardiogenic shock
* Patients whose life expectancy \<2 years
* Woman who are breastfeeding, pregnant or planning to become pregnant during study
* Patients in whom anti-platelets or heparin is contraindicated

Lesion exclusion Criteria:

* Left main culprit lesion (angiographic diameter stenosis \>50%)
* Thrombus-containing lesion
* In-stent restenosis
* Side branch lesion
* Chronic total occlusion
* Small vessel with reference diameter \<2.5mm
* Coronary spasm despite administration of nitrate
* Inability for imaging catheter to pass through tight stenosis, calcification, angulations
* Poor image quality
* Angiographically visible collateral vessels

Conditions2

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