Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study.
Xiaoya GuoAkiko MaeharaMitsuaki MatsumuraLiang WangJie ZhengHabib SamadyGary S MintzDon P GiddensDalin TangPublished in: Biomedical engineering online (2021)
This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.
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