INTelligent computeR-Aided Surgical gUidance for Robot-assisted surGEry

Oncologic operations are among the most complex surgical procedures. Esophagectomy is associated with high complication and mortality rates and the learning curve is more than 100 cases. During esophagectomy vital anatomical structures are located within the narrow operating space. This project aims to improve surgeons’ orientation and ability to recognize vital anatomical structures during complex procedures by developing machine learning for surgical phase and anatomy recognition. A laboratory proof-of-concept will be built and the targeted required accuracy and speed will be evaluated, for which videos and CT scans are available. Relevant stakeholders are involved to ensure international standardization and implementation.