PREOPERATIVE PLANNING OF MINIMALLY INVASIVE MITRAL VALVE SURGERY THROUGH DEEP-LEARNING

Background: Minimally invasive mitral valve surgery is increasingly adopted worldwide. Patient selection and preoperative surgical planning are crucial to allow the best postoperative outcomes. We propose a novel preoperative planning approach based on Deep-learning techniques, aimed at the identification of the most appropriate thoracic access for minimally invasive mitral valve surgery in a patient-specific fashion. Materials and Methods: A public Deep-Learning medical segmentation with a public dataset (TotalSegmentator) was employed. Our algorithm consisted of four steps: localization of mitral valve, calculation of the distance from mitral valve and thoracic cage, calculation of the angle between mitral valve plane and surgeon’s exposition, absence/presence of ribs. We conducted a validation process guided by a cardiac surgeon. Results: The evaluation of the deep-learning derived planning was performed on the basis of differences in thoracic distances and of angular discrepancy vs. the semiautomatic method. Finally, we defined two interfaces to navigate among candidate working ports. In the first interface it is possible to give greater importance to the distance or the angle according to surgeon’s choice. In the second interface, this choice is fixed as a preliminary setting. Conclusion: This tool may help the surgeon to identify the most appropriate working port for minimally invasive mitral valve surgery, and to predict the features of different candidate thoracic accesses in individual patients. It can also help standardizing the preoperative planning and obtaining a faster learning curve for trainee surgeons. This work is seminal, and represents an initial step towards a comprehensive planning tool for this surgery.