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<p>The use of artificial intelligence (AI) in echocardiography has been growing continuously, particularly in chamber quantification and disease detection. Although studies have validated different AI approaches for automated quantification of cardiac size and function, it is unknown whether different AI software tools provide concordant measurements. Accordingly, we aimed to: (1) evaluate the inter-software variability of the automated, AI-based measurements, and (2) compare their variability to that of conventional measurements performed by expert readers.Echocardiographic images from 116 randomly selected subjects, including 66 patients with cardiac pathology and 50 healthy volunteers were analyzed to obtain the following: (1) linear measurements: end-diastolic left ventricular internal diameter (LVIDd), basal right ventricular diameter (RVDd), and LV posterior wall thickness (LVPWd), (2) Doppler measurements (e’ lateral, e’ septal, mitral inflow E/A ratio), and (3) end-diastolic and end-systolic left ventricular volumes (LVEDV, LVESV), ejection fraction (LVEF), and end-systolic left atrial volume (LAESV). These measurements were performed by: (1) two fully automated AI-based software tools (Us2.AI and Philips), and (2) two expert readers using guidelines recommended methodology. Inter-software and inter-observer variability were assessed by percent absolute difference (PAD) and intraclass correlation coefficients (ICC) between corresponding measurements.AI measurements differed from each other, with the differences reaching significance for 6/10 parameters, while conventional measurements by experts significantly differed from each other for 3/10 parameters. Overall, the variability between AI tools was similar to the conventional methodology, with smaller PAD and higher ICC values noted for 6/10 and 2/10 parameters, respectively. Although different commercial AI-based tools for automated analysis of echocardiographic images do not provide identical results, the variability in the majority of these measurements was similar to or better than that of experts using the conventional methodology. Further studies are needed to ascertain these findings for other AI algorithms and larger numbers of readers and parameters.</p>

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Automated analysis of echocardiographic images: do different artificial intelligence tools provide concordant measurements?

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Teodora Szasz,
  • Juan I. Cotella,
  • Jeremy A. Slivnick,
  • Jia Guo,
  • Niklas Hitschrich,
  • Hendrik Wiebel,
  • Marcus Schreckenberg,
  • Federico M. Asch,
  • Victor Mor-Avi,
  • Roberto M. Lang

摘要

The use of artificial intelligence (AI) in echocardiography has been growing continuously, particularly in chamber quantification and disease detection. Although studies have validated different AI approaches for automated quantification of cardiac size and function, it is unknown whether different AI software tools provide concordant measurements. Accordingly, we aimed to: (1) evaluate the inter-software variability of the automated, AI-based measurements, and (2) compare their variability to that of conventional measurements performed by expert readers.Echocardiographic images from 116 randomly selected subjects, including 66 patients with cardiac pathology and 50 healthy volunteers were analyzed to obtain the following: (1) linear measurements: end-diastolic left ventricular internal diameter (LVIDd), basal right ventricular diameter (RVDd), and LV posterior wall thickness (LVPWd), (2) Doppler measurements (e’ lateral, e’ septal, mitral inflow E/A ratio), and (3) end-diastolic and end-systolic left ventricular volumes (LVEDV, LVESV), ejection fraction (LVEF), and end-systolic left atrial volume (LAESV). These measurements were performed by: (1) two fully automated AI-based software tools (Us2.AI and Philips), and (2) two expert readers using guidelines recommended methodology. Inter-software and inter-observer variability were assessed by percent absolute difference (PAD) and intraclass correlation coefficients (ICC) between corresponding measurements.AI measurements differed from each other, with the differences reaching significance for 6/10 parameters, while conventional measurements by experts significantly differed from each other for 3/10 parameters. Overall, the variability between AI tools was similar to the conventional methodology, with smaller PAD and higher ICC values noted for 6/10 and 2/10 parameters, respectively. Although different commercial AI-based tools for automated analysis of echocardiographic images do not provide identical results, the variability in the majority of these measurements was similar to or better than that of experts using the conventional methodology. Further studies are needed to ascertain these findings for other AI algorithms and larger numbers of readers and parameters.