Artificial Intelligence-Enhanced Echocardiographic Assessment of the Aortic Valve Stenosis Continuum

Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic valve stenosis (AVS), yet it requires skilled operators and can be resource-intensive. Objectives: To develop and validate an artificial intelligence (AI)-based system for evaluating AVS that is effective in both resource-limited and advanced settings. Methods: We created a dual-pathway AI system for AVS evaluation using a nationwide echocardiographic dataset (developmental dataset, n=8,427): 1) a deep learning (DL)-based AVS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AVS evaluation. We performed internal (internal test dataset [ITDS], n=841) and external validation (distinct hospital dataset [DHDS], n=1,696; temporally distinct dataset [TDDS], n=772) for diagnostic value across various stages of AVS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement) Results: The DL index for the AVS continuum (DLi-AVSc, range 0-100) increases with worsening AVS severity and demonstrated excellent discrimination for any AVS (AUC 0.87-0.99), significant AVS (0.93-0.97), and severe AVS (0.97). A 10-point increase in DLi-AVSc was associated with an 85% increased risk for composite endpoints in ITDS and a 53% and 59% increase in DHDS and TDDS, respectively. Automatic measurement of conventional AVS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AVS staging (98.2% for ITDS, 81.0% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. Conclusions: The AI-based system provides accurate and prognostically valuable AVS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments. ### Competing Interest Statement Y.E.Y. is Chief Medical Officer of Ontact Health, Inc. J.J., Y.J., Y.H., and S.A.L. are currently affiliated with Ontact Health, Inc. J.J., J.K., and S.A.L are co-inventors on a patent related to this work filed by Ontact Health (METHOD FOR PROVIDING INFORMATION ON SEVERITY OF AORTIC STENOSIS AND DEVICE USING THE SAME). H.J.C. holds stock in Ontact Health, Inc. The other authors have no conflicts of interest to declare. ### Funding Statement This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations 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: The institutional review board of Seoul National University Bundang Hospital and Severance Hospital approved this study and waived the requirement for informed consent because of the retrospective and observational nature of the study design. 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). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The AI-based frameworks utilized in this study were developed and validated using the Open AI Dataset Project (AI-Hub) dataset, an initiative supported by the South Korean government's Ministry of Science and ICT.