Reliability of Artificial Intelligence (AI)-Augmented Point-of-care Cardiac Ultrasound in the Hands of Internists
NCT ID: NCT05455541
Last Updated: 2024-01-03
Study Results
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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UNKNOWN
300 participants
OBSERVATIONAL
2022-08-21
2023-12-31
Brief Summary
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Up to 1000 subjects; Study population will be distributed according to the following schema:
Group 1 -up to 800 patients hospitalized in the Internal Medicine division Group 2 - up to 200 patients hospitalized in the acute Geriatric division
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Hospitalized patients with an indication for POCUS examination
Patient with an accepted indication for point of care echo study that are clinical stable.
AI augmented POCUS examination
Internists will perform POCUS studies as they consider appropriate for patient management. Interpretation of the POCUS exams will be aided by machine learning based analysis of the videos.
Interventions
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AI augmented POCUS examination
Internists will perform POCUS studies as they consider appropriate for patient management. Interpretation of the POCUS exams will be aided by machine learning based analysis of the videos.
Eligibility Criteria
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Inclusion Criteria
2. Willing and able to provide consent for a short partial transthoracic POCCUS examinations
3. No urgent or other compelling need for a comprehensive echo exam by the echo lab.
Exclusion Criteria
2. Severe thoracic deformation or thoracic wall infection or wound that does not permit an adequate echocardiographic examination
3. Very poor or non-diagnostic echocardiographic imaging quality on prior echocardiography examinations.
4. Patients after lung resection
5. Patients unable lie in bed and undergo a standard echo study
6. Participation in an interventional study
ALL
No
Sponsors
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Sheba Medical Center
OTHER_GOV
Responsible Party
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Locations
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Sheba Medical Center
Ramat Gan, , Israel
Countries
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Central Contacts
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Facility Contacts
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Robert Klempfner, Prof.
Role: primary
Role: backup
References
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Faierstein K, Fiman M, Loutati R, Rubin N, Manor U, Am-Shalom A, Cohen-Shelly M, Blank N, Lotan D, Zhao Q, Schwammenthal E, Klempfner R, Zimlichman E, Raanani E, Maor E. Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality. J Am Soc Echocardiogr. 2024 Aug;37(8):725-735. doi: 10.1016/j.echo.2024.04.017. Epub 2024 May 11.
Other Identifiers
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SHEBA-22-9419-KR-CTIL
Identifier Type: -
Identifier Source: org_study_id
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