Deep learning approach for stroke volume variation estimation: retrospective modeling and prospective deployment in real-world practice
. This study emphasizes the advantages of the DL-based model compared to ABP-PWA-based algorithms and presents prospective results that support the clinical applicability of SVVNet in the operating room. Our model has the potential to replace pre-existing monitoring systems and to enhance the complex surgical environment.
Authors
- H. Kim
- Jae-Man Shin (ORCID: https://orcid.org/0009-0005-4808-501X)
- Junik Park (ORCID: https://orcid.org/0009-0001-9310-7557)
- Woo-Young Seo
- Sung-Hoon Kim
Institutions
- Asan Medical Center (KR)
- University of Ulsan (KR)
- LG (South Korea) (KR)
Publication Details
- Journal
- Physiological Measurement
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1088/1361-6579/ae4cee
- Primary Topic
- Hemodynamic Monitoring and Therapy
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Asan Institute for Life Sciences, Asan Medical Center