Background: Endoscopic mitral valve surgery is becoming standard of care. However it has steep learning curve and annular sizing is dicult with conventional sizers.

Methods: A retrospective analysis was performed on 104 patients who underwent mitral repair at one institution from July 2015 to September 2022. 93 of them had CT scans suitable for mitral valvular apparatus measurements (intertrigonal distance, anterior leaet max length, etc). A machine learning pipeline was created in order to predict annuloplasty band size (from 26 to 38 mm). Mitral measurements were done in MPR modality.

Results: A nal model (best model among the trained) was able to predict the annular size with 72% accuracy. A linear combination (PCA fo two components) of intertrigonal distance and anterior leaet lengths was the main linear combination in the model

Conclusions: Linear combination of mitral valve measurements can predict annular size in clinical setting. Its value is highly valuable for young and inexperienced groups.