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Background <p>Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measurements and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited.</p> Methods <p>This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025).</p> Results <p>Median analysis time was 94&#xa0;s (interquartile range [IQR], 82–106&#xa0;s) for the AI workflow versus 490&#xa0;s (IQR, 438–626&#xa0;s) for the human workflow (P &lt; 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P &lt; 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indices ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579).</p> Conclusions <p>Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.</p>

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Fully automated artificial intelligence–based echocardiographic analysis substantially reduces workflow time while preserving measurement accuracy: a pilot study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Jonghee Sun,
  • Yeonyee E. Yoon,
  • Jiyeon Lee,
  • Ganghan Lee,
  • Minjung Bak,
  • Jiesuck Park,
  • Hong-Mi Choi,
  • In-Chang Hwang,
  • Goo-Yeong Cho

摘要

Background

Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measurements and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited.

Methods

This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025).

Results

Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indices ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579).

Conclusions

Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.