Adequate blood flow through the coronary tree is critical for maintaining cardiac perfusion. Coronary angiography has the potential to provide rich and dynamic hemodynamic information, however, current strategies to assess flow through the coronary vessels depend on either subjective expert opinion (TIMI flow grade) or laborious frame-by-frame anatomical analysis (TIMI frame count and quantitative flow ratio). Here we present a strategy for automated characterization of bulk flow through the coronary tree using the right coronary artery as an example. We leverage the AngioNet neural network to generate sequential segmentations of angiograms, create time series of the summed segmentation intensities (i.e., a contrast intensity profile), and quantitatively characterize the filling and washout phases of these intensity profiles. We demonstrate that AngioNet-derived frame counts and normalized mean filling slopes of contrast intensity profiles correlate well with manual frame counts and flow grades in both our derivation and validation datasets. Furthermore, the generated washout dynamics appear to provide different information to the traditional frame count and flow grade metrics, which only deal with the initial filling phase, suggesting that washout dynamics of the contrast intensity profiles may capture novel information about the coronary microcirculation.

BN and AFA are cofounders of AngioInsight Inc., a company developing AI software to process angiographic images of patients with coronary artery disease. IA was employed part-time at AngioInsight from Aug, 2022 through July 2023. AngioInsight did not sponsor this work. This work is a part of a patent which has been filed by the University of Michigan, on which BN, AFA, IA, and JR are listed co-inventors. AP declares no competing interests.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

A dataset of de-identified RCA angiograms of left anterior oblique (LAO) projections previously used as part of the AngioNet training cohort was the starting point of the current study. This dataset was acquired using a Siemens Artis Q Angiography system under study protocol (HUM00084689) approved by the Institutional Review Board of the University of Michigan Medical School (IRBMED). With low risk for subject harm and retrospective use of data collected for clinical purposes, IRBMED approved use without requiring informed consent.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors. The trained weights of the AngioNet Neural Network are proprietary property of AngioInsight and cannot be shared.