Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context - Jiesuck Park; Jiyeon Kim; Jaeik Jeon; Yeonyee E. Yoon; Yeonggul Jang; Hyunseok Jeong; Youngtaek Hong; Seung-Ah Lee; Hong-Mi Choi; In-Chang Hwang; Goo-Yeong Cho; Hyuk-Jae Chang (EBioMedicine)
Summary: Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings. Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. We performed internal (internal test dataset [ITDS], n = 841) and external validation (distinct hospital dataset [DHDS], n = 1696; temporally distinct dataset [TDDS], n = 772) for diagnostic value across various stages of AS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement). Findings: The DL index for the AS continuum (DLi-ASc, range 0–100) increased with worsening AS severity and demonstrated excellent discrimination for any AS (AUC 0.91–0.99), significant AS (0.95–0.98), and severe AS (0.97–0.99). DLi-ASc was independent predictor for composite endpoint (adjusted hazard ratios 2.19, 1.64, and 1.61 per 10-point increase in ITDS, DHDS, and TDDS, respectively). Automatic measurement of conventional AS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AS staging (98.2% for ITDS, 82.1% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. Interpretation: The AI-based system provides accurate and prognostically valuable AS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments. Funding: 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; MSIT, Republic of Korea) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA) funded by the MSIT, Republic of Korea (Grant No.: H0904-24-1002).