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<p>Echocardiography traditionally requires experienced operators to select and interpret clips from specific viewing angles. Clinical decision-making is therefore limited for handheld cardiac ultrasound (HCU), which is often collected by novice users. In this study, we developed a view-flexible deep learning framework to estimate left ventricular ejection fraction (LVEF), patient age, and patient sex from any of several views containing the left ventricle. Model performance was: (1) consistently strong across retrospective transthoracic echocardiography (TTE) datasets; (2) comparable between prospective HCU versus TTE (625 patients; LVEF <i>r</i><sup>2</sup> 0.80 vs. 0.86, LVEF [&gt; or ≤40%] AUC 0.981 vs. 0.993, age <i>r</i><sup>2</sup> 0.85 vs. 0.87, sex classification AUC 0.985 vs. 0.996); (3) comparable between prospective HCU data collected by experts versus novice users (100 patients; LVEF <i>r</i><sup>2</sup> 0.77 vs. 0.64, LVEF AUC 0.983 vs. 0.968). This approach may broaden the clinical utility of echocardiography by lessening the need for user expertise in image acquisition.</p><p></p>

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A view-flexible deep learning framework for automated analysis of 2D echocardiography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

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  • D. M. Anisuzzaman,
  • Jeffrey G. Malins,
  • John I. Jackson,
  • Eunjung Lee,
  • Jwan A. Naser,
  • Behrouz Rostami,
  • Jared G. Bird,
  • Dan Spiegelstein,
  • Talia Amar,
  • Christie C. Ngo,
  • Jae K. Oh,
  • Patricia A. Pellikka,
  • Jeremy J. Thaden,
  • Francisco Lopez-Jimenez,
  • Timothy J. Poterucha,
  • Paul A. Friedman,
  • Sorin V. Pislaru,
  • Garvan C. Kane,
  • Zachi I. Attia

摘要

Echocardiography traditionally requires experienced operators to select and interpret clips from specific viewing angles. Clinical decision-making is therefore limited for handheld cardiac ultrasound (HCU), which is often collected by novice users. In this study, we developed a view-flexible deep learning framework to estimate left ventricular ejection fraction (LVEF), patient age, and patient sex from any of several views containing the left ventricle. Model performance was: (1) consistently strong across retrospective transthoracic echocardiography (TTE) datasets; (2) comparable between prospective HCU versus TTE (625 patients; LVEF r2 0.80 vs. 0.86, LVEF [> or ≤40%] AUC 0.981 vs. 0.993, age r2 0.85 vs. 0.87, sex classification AUC 0.985 vs. 0.996); (3) comparable between prospective HCU data collected by experts versus novice users (100 patients; LVEF r2 0.77 vs. 0.64, LVEF AUC 0.983 vs. 0.968). This approach may broaden the clinical utility of echocardiography by lessening the need for user expertise in image acquisition.