Safety and Efficacy Study of AI LVEF

NCT05140642 | Completed

• 1 other identifier: STUDY00001707
• Study Type: interventional
• Target: 3,495
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Heart Failure, Systolic; Heart Failure, Diastolic

Keywords

Echocardiogram; Artificial Intelligence

Outcome Measures

Primary Outcomes (2)

• Frequency of >5% change in LVEF between preliminary and final report

  Proportion of studies the LVEF is changed more than 5% in final report

  10 Minutes

• Average change in LVEF between preliminary and final report

  Mean change in LVEF between preliminary and final report

  10 Minutes

Secondary Outcomes (2)

• Frequency cardiologist adjusts preliminary annotation

  10 Minutes

• Average change in LVEF between prior clinical report and final report

  10 Minutes

Study Arms (2)

Sonographer Annotation

ACTIVE COMPARATOR

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

Artificial Intelligence Annotation

EXPERIMENTAL

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

Interventions

Automated annotation of the left ventricle through deep learning 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.

Artificial Intelligence Annotation

Sonographer Measurement of LVEF OTHER

Standard practice sonographer measurement of left ventricle and assessment of LVEF

Sonographer Annotation

Eligibility Criteria

• Age: 18 Years - 110 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Cedars-Sinai Medical Center: lead

Study Sites (1)

Cedars-Sinai Medical Center

Los Angeles, California, 90048, United States

Location

Related Publications (2)

• Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, Heidenreich PA, Harrington RA, Liang DH, Ashley EA, Zou JY. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020 Apr;580(7802):252-256. doi: 10.1038/s41586-020-2145-8. Epub 2020 Mar 25.

  PMID: 32269341 RESULT

• He B, Kwan AC, Cho JH, Yuan N, Pollick C, Shiota T, Ebinger J, Bello NA, Wei J, Josan K, Duffy G, Jujjavarapu M, Siegel R, Cheng S, Zou JY, Ouyang D. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023 Apr;616(7957):520-524. doi: 10.1038/s41586-023-05947-3. Epub 2023 Apr 5.

  PMID: 37020027 DERIVED

MeSH Terms

Conditions

Heart Failure, Systolic; Heart Failure, Diastolic

Condition Hierarchy (Ancestors)

Heart Failure; Heart Diseases; Cardiovascular Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: 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.
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Staff Physician

Model Details: 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.

Study Record Dates

• First Submitted: November 17, 2021
• First Posted: December 1, 2021
• Study Start: April 1, 2022
• Primary Completion: June 29, 2022
• Study Completion: June 29, 2022
• Last Updated: July 5, 2022

Record last verified: 2022-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)