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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COMPLETED
120 participants
OBSERVATIONAL
2017-01-04
2018-04-30
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Interventions
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Automated Heart Murmur Detection AI
Automated AI algorithm-based analysis of digital heart sound recordings to detect and classify heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. AI algorithm results for each recording include: AHA classification (Class I (pathologic heart murmur) versus class III (innocent heart murmur or no heart murmur), murmur timing, murmur grade, heart rate and S1/S2 identification.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* Patients who are being followed for known congenital heart disease and are returning for follow up
* Patients referred for a suspected heart murmur
Exclusion Criteria
* Patient whose behaviour does not allow for a standard auscultation by the physician (e.g. a screaming fit).
1 Day
ALL
Yes
Sponsors
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CSD Labs GmbH
OTHER
Responsible Party
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Principal Investigators
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Lillian Lai, MD
Role: PRINCIPAL_INVESTIGATOR
Children's Hopsital of Eastern Ontario, Canada
Locations
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Children's Hospital of Eastern Ontario
Ottawa, , Canada
Countries
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References
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Lai LS, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study. Congenit Heart Dis. 2016 Sep;11(5):386-395. doi: 10.1111/chd.12328. Epub 2016 Mar 15.
Related Links
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Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study.
Other Identifiers
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OTT03
Identifier Type: -
Identifier Source: org_study_id
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