First RCT evidence for use of AI in daily practice

After blinded review of initial LVEF assessment, cardiologists were less likely to substantially change final reports with initial AI than sonographer assessment.

A first and only randomised-controlled trial of its kind

While tremendous progress in applying AI to cardiology has been made, no blinded randomised studies have yet provided high-level evidence comparing its use to standard-of-care sonographer assessments. The EchoNet-RCT (NCT05140642) met that challenge; the findings were reported by Dr David Ouyang (Cedars-Sinai Smidt Heart Institute, CA, USA)1.

The researchers measured how often cardiologists changed the initial assessment by AI compared with how often they changed the initial assessment by sonographer. The AI element made use of a deep learning algorithm called EchoNet-Dynamic, which was trained on echocardiogram videos to assess cardiac function and was previously shown to assess LVEF, using information across multiple cardiac cycles to minimise error and produce consistent results2. The primary endpoint was the frequency of a >5% change in LVEF between the initial assessment (AI or sonographer) and the final cardiologist report. The trial was designed to test for non-inferiority, with a secondary objective of testing for superiority.

In certain medical applications, AI is "ready for primetime"

Transthoracic echocardiograms (n=3,495) were performed on adults for any clinical indication, and then randomised for LVEF assessment to either AI or an experienced sonographer. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference -10.4%, 95% CI -13.2% to -7.7%; P<0.001 for non-inferiority, P<0.001 for superiority). The safety endpoint was the difference between the final cardiologist report and a historical cardiologist report. The mean absolute difference was 6.3% in the AI group and 7.2% in the sonographer group (difference -0.96%; 95% CI -1.34% to -0.54%; P<0.001 for superiority).

In conclusion, Dr Ouyang said: “We were initially quite conservative. This was built as a non-inferiority study, but it actually met that endpoint as well as the endpoint of superiority, so we were pleasantly surprised to see that it was able to work so well. This shows that AI in certain cases is ready for primetime.”

  1. Ouyang D, et al. EchoNet-RCT - Safety and Efficacy Study of AI LVEF. Hot Line Session 3, ESC Congress 2022, Barcelona, Spain, 26–29 August.
  2. Ouyang D, et al. Nature. 2020;580(7802):252–256.