Cedars-Sinai Medical Center
David Ouyang
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.
Heart Failure, Systolic
Heart Failure, Diastolic
Automated annotation of the left ventricle through deep learning
Sonographer Measurement of LVEF
Not Applicable
| Study Type : | Interventional |
| Estimated Enrollment : | 3495 participants |
| Masking : | Single |
| Masking Description : | 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. |
| Primary Purpose : | Diagnostic |
| Official Title : | Blinded Randomized Controlled Trial of Artificial Intelligence Guided Assessment of Cardiac Function |
| Actual Study Start Date : | April 1, 2022 |
| Estimated Primary Completion Date : | June 29, 2022 |
| Estimated Study Completion Date : | June 29, 2022 |
| Arm | Intervention/treatment |
|---|---|
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Active Comparator: 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. |
Other: Sonographer Measurement of LVEF |
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Experimental: 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. |
Other: Automated annotation of the left ventricle through deep learning |
| Ages Eligible for Study: | 18 Years to 110 Years |
| Sexes Eligible for Study: | All |
| Accepts Healthy Volunteers: | No |
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Cedars-Sinai Medical Center
The Angels, California, United States, 90048