Key result
This paper presents a study protocol to develop and validate an artificial intelligence and cloud-based platform for fully automated percutaneous coronary intervention guidance using coronary angiography.
Why the study?
Ischemic heart disease represents a heavy clinical burden, motivating the development of automated tools to optimize PCI planning.
Does an artificial intelligence and cloud-based platform for fully automated PCI guidance improve clinical outcomes and accurately predict post-PCI FFR in patients with acute coronary syndromes?
Does an artificial intelligence and cloud-based platform for fully automated PCI guidance improve clinical outcomes and accurately predict post-PCI FFR in patients with acute coronary syndromes?
This study protocol outlines the development and validation of an artificial intelligence and cloud-based platform for fully automated PCI guidance, including 3D reconstruction and virtual post-PCI FFR computation from coronary angiography.
Protocol for AI-based PCI guidance platform leaves open clinical utility and FFR accuracy; prospective trials needed before adoption.
Ischemic heart disease represent a heavy burden for the medical systems irrespective of the methods used for diagnosis and treatment of such patients in the daily medical routine. The present paper depicts the protocol of a study whose main aim is to develop, implement and test an artificial intelligence algorithm and cloud based platform for fully automated PCI guidance using coronary angiography images. We propose the utilisation of multiple artificial intelligence based models to produce three-dimensional coronary anatomy reconstruction and assess function- post-PCI FFR computation- for developing an extensive report describing and motivating the optimal PCI strategy selection. All the relevant artificial intelligence model outputs (anatomical and functional assessment-pre- and post-PCI) are presented to the clinician via a cloud platform, who can then take the utmost treatment decision. The physician will be provided with multiple scenarios and treatment possibilities for the same case allowing a real-time evaluation of the most appropriate PCI strategy planning and follow-up. The artificial intelligence algorithms and cloud based PCI selection workflow will be verified and validated in a pilot clinical study including subjects prospectively to compare the artificial intelligence services and results against annotations and invasive measurements.
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Ploscaru et al. (2022) studied Acute coronary syndromes (n=240). Artificial intelligence and cloud based platform for fully automated PCI guidance vs. Annotations and invasive measurements was evaluated on Verification and validation of AI models against annotations and invasive measurements. This paper presents a study protocol to develop and validate an artificial intelligence and cloud-based platform for fully automated percutaneous coronary intervention guidance using coronary angiography.
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