Deep Learning‐Driven Automated Assessment of Left Ventricular Diastolic Function in Echocardiography
Automated AI-based evaluation of diastolic function using 2D TTE is feasible and provides accurate, guideline-based measurements with strong correlation to expert interpretation. The software demonstrated promising results in classifying LVDF grades, particularly in distinguishing normal and severe dysfunction. This approach shows potential to enhance diagnostic consistency and efficiency in clinical practice, although further validation is needed.
Authors
- Gintarė Šakalytė (ORCID: https://orcid.org/0000-0001-8339-0013)
- Gediminas Jaruševičius (ORCID: https://orcid.org/0000-0001-9205-1902)
- Dovydas Verikas (ORCID: https://orcid.org/0000-0001-5937-745X)
- Arnas Karužas (ORCID: https://orcid.org/0000-0002-8441-0108)
- Karolis Šablauskas (ORCID: https://orcid.org/0000-0003-4166-0331)
- Ieva Kažukauskienė (ORCID: https://orcid.org/0000-0002-0619-3881)
- Vaiva Lesauskaitė (ORCID: https://orcid.org/0000-0003-2736-3111)
- Antanas Kiziela (ORCID: https://orcid.org/0000-0003-1754-1068)
- Jurgita Plisienė
- L Miscikas
- K Naskauskas
Institutions
- Lithuanian University of Health Sciences (LT)
- Vilnius University (LT)
- Hospital of Lithuanian University of Health Sciences Kaunas Clinics (LT)
- Vilnius University of Applied Sciences (LT)
Publication Details
- Journal
- Echocardiography
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1111/echo.70290
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00