Segmentation and quantification of atherosclerotic plaques in optical coherence tomography
This study presents a robust ML-driven framework for automated OCT segmentation that uses a hybrid approach and weighted ensemble learning to address class imbalance and feature complexity, significantly improving the accuracy and efficiency of atherosclerotic plaque analysis. The findings suggest potential clinical implications, including improved detection of high-risk plaques and enhanced decision-making in cardiovascular care. However, further prospective validation is required before clinical adoption. Future research should focus on expanding datasets, integrating multimodal imaging, and refining models for real-time clinical use, paving the way for transformative advancements in cardiovascular diagnostics.
Authors
- V.V. Laptev (ORCID: https://orcid.org/0000-0001-8639-8889)
- N.V. Litvinyuk (ORCID: https://orcid.org/0000-0002-0630-7244)
- Вячеслав Данилов
- K. Yu. Klyshnikov (ORCID: https://orcid.org/0000-0003-3211-1250)
- И. С. Бессонов (ORCID: https://orcid.org/0000-0003-0578-5962)
- Е. А. Овчаренко (ORCID: https://orcid.org/0000-0001-7477-3979)
- N. А. Kochergin (ORCID: https://orcid.org/0000-0002-1534-264X)
Institutions
- Universitat Pompeu Fabra (ES)
- Siberian State Medical University (RU)
- University of Tyumen (RU)
- Krasnoyarsk Regional Clinical Hospital (RU)
- Research Institute of Complex Problems of Cardiovascular Disease Russian Academy of Medical Sciences (RU)
- Kemerovo State University (RU)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.111061
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00