AI System Shows Consistent Accuracy in Echocardiogram Interpretation: JAMA

The performance of PanEcho was assessed using two main metrics:

• Area Under the Receiver Operating Characteristic Curve (AUC): For diagnostic classification.

• Mean Absolute Error (MAE): For estimation of echocardiographic parameters.

Key Findings

Diagnostic Accuracy:

• PanEcho performed 18 diagnostic classification tasks with a median AUC of 0.91 (interquartile range [IQR]: 0.88–0.93) in internal validation.

Parameter Estimation:

• For 21 echocardiographic measurements, the median normalized mean absolute error was 0.13 (IQR: 0.10–0.18).

• Left ventricular ejection fraction (LVEF): Estimated with mean absolute error of 4.2% (internal) and 4.5% (external).

• Detection of moderate or greater left ventricular systolic dysfunction: AUC of 0.98 (internal) and 0.99 (external).

• Right ventricular systolic dysfunction: AUC of 0.93 (internal) and 0.94 (external).

• Severe aortic stenosis: AUC of 0.98 (internal) and 1.00 (external).

Performance in Limited Protocols:

• Abbreviated TTE cohort: 15 diagnosis tasks, median AUC of 0.91 (IQR: 0.87–0.94)

• Emergency department point-of-care cohort: 14 diagnosis tasks, median AUC of 0.85 (IQR: 0.77–0.87).

In conclusion, the present study showed that the AI model PanEcho was able to accurately interpret a large set of echocardiographic parameters and diagnostic labels at high rates in various real-world clinical environments. The results substantiate the future integration of PanEcho into standard cardiovascular imaging practice, especially as a tool to enhance efficiency and consistency in reporting echocardiograms.

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