Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements
Machine learning on echocardiographic parameters can detect patients with CA with accuracy. Our model identified several features that were major contributors towards identifying CA including GLS, mitral A peak velocity and LVPWd. Further study is needed to evaluate its external validity and application in clinical settings.
Authors
- James Zou (ORCID: https://orcid.org/0000-0001-8880-4764)
- Piero Ricchiuto (ORCID: https://orcid.org/0000-0002-8525-6624)
- Gloria J. Hong (ORCID: https://orcid.org/0000-0003-1572-2359)
- Michael Abiragi (ORCID: https://orcid.org/0000-0003-2824-9206)
- Rachel S. Chang (ORCID: https://orcid.org/0000-0002-8969-2757)
- Louie Cao (ORCID: https://orcid.org/0000-0002-9680-5801)
- Jonathan Le
- Phillip Tacon
- Chathuri Daluwatte
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
- I-Min Chiu (ORCID: https://orcid.org/0000-0001-6876-8217)
Institutions
- Cedars-Sinai Medical Center (US)
- City College of San Francisco (US)
- Alexion Pharmaceuticals (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Open Heart
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1136/openhrt-2024-002884
- Citations
- 5
- Primary Topic
- Amyloidosis: Diagnosis, Treatment, Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.17
Funders
- Pfizer
- Alexion Pharmaceuticals