Safety and Efficacy Study of AI LVEF (EchoNet-RCT)

Cedars-Sinai Medical Center

Status: Completed

Conditions: Heart Failure, Diastolic; Heart Failure, Systolic

Treatments: Other: Sonographer Measurement of LVEF; Other: Automated annotation of the left ventricle through deep learning

Study type: Interventional

Funder types: Other

Identifiers

NCT05140642

STUDY00001707

Details and patient eligibility

About

To determine whether an integrated AI decision support can save time and improve accuracy of assessment of echocardiograms, the investigators are conducting a blinded, randomized controlled study of AI guided measurements of left ventricular ejection fraction compared to sonographer measurements in preliminary readings of echocardiograms.

Enrollment: 3,495 patients

Sex: All

Ages: 18 to 110 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• The study imaging studies will include patients who underwent imaging (limited or comprehensive transthoracic echocardiogram studies) and a LVEF was adjudicated in the echocardiography/non-invasive cardiac imaging laboratory.
• The study participants are cardiologists reading in the echocardiography/non-invasive cardiac imaging laboratory.

Exclusion criteria

• The study imaging studies will exclude transesophageal echocardiogram imaging.
• The study will exclude cardiologists who decline to participate

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Single Group Assignment

Masking: Single Blind

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3,495 participants in 2 patient groups

Sonographer Annotation

Active Comparator group

Description:

Currently, sonographer technicians provide preliminary interpretations prior to validation and overreading by cardiologists. This staggered, stepwise evaluation allows for the introduction of AI decision support with minimal impact on patient care. Physicians are already used to adjusting the preliminary report given the variable training of sonographers and on the lookout for changes, variation, or adjustments that need to be made.

Treatment:

Other: Sonographer Measurement of LVEF

Artificial Intelligence Annotation

Experimental group

Description:

In preliminary work, a novel AI algorithm developed to assess LVEF was shown to be more precise than human interpretation in 10,030 echocardiograms done at Stanford University (Ouyang et al. Nature, 2020). With randomization, a proportion of the preliminary interpretations will be done by AI technology and the study team will assess how different this preliminary interpretation is from the final interpretation.

Treatment:

Other: Automated annotation of the left ventricle through deep learning