Detection of Undiagnosed Liver Cirrhosis via Artificial Intelligence-Enabled Electrocardiogram (DULCE): Rationale and design of a pragmatic cluster randomized clinical trial
Preliminary findings suggest outstanding potential for the use of an ECG-enabled machine learning algorithm for detection of advanced CLD in the primary care community.
Authors
- Patrick S. Kamath (ORCID: https://orcid.org/0000-0002-7888-1165)
- Puru Rattan (ORCID: https://orcid.org/0000-0001-7410-488X)
- Amy Olofson (ORCID: https://orcid.org/0000-0001-7212-8132)
- Paul A. Friedman (ORCID: https://orcid.org/0000-0001-5052-2948)
- Alina M. Allen (ORCID: https://orcid.org/0000-0002-8393-8410)
- Peter A. Noseworthy (ORCID: https://orcid.org/0000-0002-4308-0456)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Vijay H. Shah (ORCID: https://orcid.org/0000-0001-7620-573X)
- David Rushlow (ORCID: https://orcid.org/0000-0001-9071-1516)
- Douglas A. Simonetto (ORCID: https://orcid.org/0000-0003-4095-8144)
- Ryan J. Lennon (ORCID: https://orcid.org/0000-0002-8366-8422)
- Blake Kassmeyer
- Joseph Ahn (ORCID: https://orcid.org/0000-0001-6994-2870)
- Kan Liu (ORCID: https://orcid.org/0000-0001-5625-8180)
Institutions
- Mayo Clinic (US)
- Mayo Clinic in Arizona (US)
Publication Details
- Journal
- Contemporary Clinical Trials Communications
- Published
- 2025-05-16
- DOI
- https://doi.org/10.1016/j.conctc.2025.101494
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Mayo Clinic