Safety and Efficacy Study of AI LVEF

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

3495 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-04-01

Study Completion Date

2022-06-29

Brief Summary

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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.

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Detailed Description

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Conditions

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Heart Failure, Systolic Heart Failure, Diastolic

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

SINGLE_GROUP

Studies will be randomized 1:1 to either sonographer preliminary report finding or AI preliminary report finding with final adjudication by the cardiologist. With AI preliminary report, the preliminary interpretations will be generated by AI (artificial intelligence) technology \[a semantic segmentation model\] and the PACS system's native EF calculation workflow will be used to calculate LVEF. The study team will assess how much cardiologists edit and change this preliminary interpretation is from the final interpretation.

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Participants

Measurements shown in Picture Archiving and Communication System (PACS) without direct communication between sonographer and cardiologist. Annotations are shown without identifiers on how the annotations were done. Cardiologists are blinded to source of preliminary interpretation.

Study Groups

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Sonographer Annotation

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.

Group Type ACTIVE_COMPARATOR

Sonographer Measurement of LVEF

Intervention Type OTHER

Standard practice sonographer measurement of left ventricle and assessment of LVEF

Artificial Intelligence Annotation

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.

Group Type EXPERIMENTAL

Automated annotation of the left ventricle through deep learning

Intervention Type OTHER

A semantic segmentation deep learning model will identify the left ventricle and label the left ventricle. The AI model will produce an assessment of LVEF using video based features.

Interventions

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Automated annotation of the left ventricle through deep learning

A semantic segmentation deep learning model will identify the left ventricle and label the left ventricle. The AI model will produce an assessment of LVEF using video based features.

Intervention Type OTHER

Sonographer Measurement of LVEF

Standard practice sonographer measurement of left ventricle and assessment of LVEF

Intervention Type OTHER

Eligibility Criteria

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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.