Comprehensive Summary
Jeganathan et al. evaluated the reproducibility of an AI-based automated software, eSie Valve (Siemens Healthcare) for mitral valve (MV) analysis using intraoperative 3D transesophageal echocardiography (TEE) data. The study retrospectively analyzed 36 end-systolic frames from four patients with normal valves undergoing coronary artery bypass graft surgery. Three independent examiners used the automated system to quantify six geometric parameters of MV anatomy. Mixed-effects ANOVA models assessed the influence of examiner, patient, and image loop on measurement variability, with Bonferroni correction (significance = 0.0083). Examiner effects were nonsignificant across all parameters (e.g., annular anteroposterior P = 0.25; area P = 0.07), whereas patient and loop effects were significant (P < 0.0083). These findings confirm strong interobserver reproducibility of the automated AI-based system and the reliability of its outputs.
Outcomes and Implications
This early investigation demonstrated that AI-driven echocardiographic software can generate consistent and reproducible geometric MV measurements with minimal user intervention. Such automation may reduce manual error, accelerate workflow, and support quantitative intraoperative decision-making; as percutaneous MV interventions expand and visual valve inspection becomes limited, this support is especially valuable. The authors acknowledge that small sample size, static-frame analysis, and limited patient diversity restrict generalizability, but emphasize that the feasibility of integrating self-learning algorithms to further improve accuracy. The study positions automated 3D TEE analysis as a precursor to real-time, machine-learning–assisted cardiac imaging for precise structural intervention.