Clinical integration of AI in echocardiography

Product: Us2.vi Company: Us2.ai

Integrating artificial intelligence into an echocardiography department: Feasibility and comparative study of automated versus human measurements in a high-volume clinical setting

ScienceDirect, 2025

Abstract

Background

Echocardiography is an important diagnostic tool in cardiology as it is essential for heart disease treatment. However, its time-consuming nature and reliance on user expertise constitutes a challenge for its use in high-volume clinics. Artificial intelligence (AI) offers the potential to automate tasks performed manually by echocardiographers and promises to improve efficiency and diagnostic consistency.

Aims

To evaluate the integration of AI-based tools in a high-volume echocardiography department and assess the concordance of AI-generated measurements with manually-performed measurements.

Methods

The study was conducted in the echocardiography department of Bordeaux University Hospital. Over 2 months, 894 echocardiograms were performed by operators with three experience levels (nurses, residents and experts), with measurements performed by AI and humans. The statistical analyses assessed measurement agreement between both.

Results

The AI system was successfully integrated into the hospital's infrastructure within 6 weeks. Concordance analysis revealed good to very good agreement between AI and human measurements for most parameters, especially for ejection fraction (intraclass correlation coefficient [ICC]: 0.81, 95% confidence interval [95% CI]: 0.78–0.85) and Doppler-based flow measurements (mitral E wave velocity: ICC 0.97, 95% CI 0.95–0.98). Bland-Altman analysis showed a global mean difference of −4% with a standard deviation of 15%. Subgroup analysis revealed higher concordance for experts and residents compared with nurses (mean ICCs: 0.78 and 0.79 vs. 0.72, respectively).

Conclusion

AI can be effectively integrated into clinical echocardiography practice, with high agreement between AI and human measurements. Further research is needed to investigate the long-term impact on clinical outcomes and efficiency.

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