Deep learning for estimating right ventricular function from routine coronary angiography
A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model's performance improved further by adding ECG to its inputs.
Authors
- R Gulati
- Behrouz Rostami (ORCID: https://orcid.org/0000-0002-1091-0388)
- Shih-Sheng Chang
- Kenneth A. Fetterly (ORCID: https://orcid.org/0000-0001-8609-4308)
- Jeremy J. Thaden (ORCID: https://orcid.org/0000-0003-4489-2349)
- Puskar Bhattarai (ORCID: https://orcid.org/0000-0002-5613-7028)
- Eunjung Lee (ORCID: https://orcid.org/0000-0002-6476-9527)
- Mohammad Sarraf (ORCID: https://orcid.org/0000-0002-7949-8002)
- Zachi Itzhak Attia
- Abdullah Al-Abcha
- Paul A Friedman
- Mohamad Alkhouli
Institutions
- Mayo Clinic (US)
- China Medical University (TW)
- China Medical University Hospital (TW)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1093/ehjdh/ztag004
- Primary Topic
- Pulmonary Hypertension Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00