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DOI 10.1016/j.cjca.2026.02.009

Медицинская библиотека · The Canadian journal of cardiology · 2026

Left Ventricular Ejection Fraction Reporting Variability and AI-Assisted Reproducibility: A Multicentre Analysis.

Le Yinghui, Zhou Jiali, Yang Shuna, An Jing, Li Zhiqiang, Li Yimin, Zhang Ting, Zhang Jianying, Zhang Jixin, Ju Zhiguo, Li Guang, Li Jinchen, Song Xiantao, He Yi, Li Debiao

СтатьяМногоцентровое исследованиеПеревод пока не подготовлен

Аннотация

BACKGROUND

Artificial intelligence (AI)-assisted assessment of left ventricular ejection fraction (LVEF) has been increasingly adopted in clinical practice. In this study we aimed to assess the reliability and reproducibility of AI-assisted LVEF assessment in a diverse, real-world, multicentre setting.

METHODS

We conducted a retrospective multicentre study involving 354 cardiac magnetic resonance examinations. A standardized LVEF reassessment (M-LVEF) was performed using QMass 8.1 (Medis Medical Imaging, Leiden, Netherlands) and systematically compared with original report-derived LVEF values (R-LVEF) generated using vendor-specific AI tools. For interobserver reproducibility assessment, 3 operators independently analyzed 30 randomly selected cases using fully manual and AI-assisted modes, the latter also performed with QMass 8.1 software. Operator A performed 2 measurements in both modes for intraobserver analysis.

RESULTS

In overall and single-centre analyses, the consistency between R-LVEF and M-LVEF was good or excellent (intraclass correlation coefficient ≥ 0.86), but the 95% limits of agreement all exceeded ± 5%. In 44.3% of cases > 5% differences between R-LVEF and M-LVEF were observed, and 17.5% showed > 10% differences. The AI-assisted method not only significantly reduced LVEF assessment time compared with manual analysis, but also showed superior reproducibility. This was evidenced by greater interobserver agreement among 3 operators (intraclass correlation coefficients, 0.968, 0.974, 0.984 for AI vs 0.913, 0.924, 0.971 for manual, respectively) and greater intraobserver reproducibility, all with significantly lower coefficients of variation (5.6%, 8.8%, 9.3% vs 2.5%, 3.8%, 4.4%, respectively; 3.5% vs 1.0% [P < 0.05).

CONCLUSIONS

There are individual differences in AI-assisted LVEF assessment, but it has significant advantages in efficiency and reproducibility, making AI a worthwhile tool to facilitate cardiac magnetic resonance quantification.

Для цитирования: Le Yinghui, Zhou Jiali, Yang Shuna, An Jing, Li Zhiqiang, Li Yimin, Zhang Ting, Zhang Jianying, Zhang Jixin, Ju Zhiguo, Li Guang, Li Jinchen, Song Xiantao, He Yi, Li Debiao. Left Ventricular Ejection Fraction Reporting Variability and AI-Assisted Reproducibility: A Multicentre Analysis.. The Canadian journal of cardiology. 2026. DOI: 10.1016/j.cjca.2026.02.009.

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