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. 2022 Sep 9;17(9):e0274296.
doi: 10.1371/journal.pone.0274296. eCollection 2022.

Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol

Vlad Ploscaru  1 , Nicoleta-Monica Popa-Fotea  1   2 , Lucian Calmac  1 , Lucian Mihai Itu  3   4 , Cosmin Mihai  1 , Vlad Bataila  1 , Bogdan Dragoescu  1 , Andrei Puiu  3   4 , Cosmin Cojocaru  1   2 , Minoiu Aurelian Costin  1 , Alexandru Scafa-Udriste  1   2
Affiliations

Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol

Vlad Ploscaru et al. PLoS One. .

Abstract

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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Conflict of interest statement

The authors have declared no competing interests exist.

Figures

Fig 1
Fig 1
(a) Annotations of large (green) and small vessels (red), (b) Overlay of the input frame and the predicted probability map.
Fig 2
Fig 2. Sample envisaged detection of the distal point (white circle) for a right coronary artery image.
The black circles the catheter tip.
Fig 3
Fig 3. Approach for artificial intelligence based virtual post-percutaneous coronary intervention fractional flow reserve (FFR) computation.
Fig 4
Fig 4. Cycle GAN approach for performing style transfer between healthy and pathological coronary angiography images slices.
Fig 5
Fig 5. Overall concept and specific components for developing the cloud based solution.
Fig 6
Fig 6. Graphic processing unit instance orchestration on the cloud.
See this image and copyright information in PMC

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