A Machine Learning Method for Automated Intraoperative Assessments in Total Hip Surgery

This research project aims to create advanced computer tools to help surgeons during hip replacement
surgery by automatically identifying important parts of the hip anatomy and providing accurate
measurements in real-time. The intern(s) will work on developing a large, realistic set of simulated X-raylike
images to mimic what surgeons see during different stages of the procedure. They will also design and
train neural network computer models to analyze these images, helping surgeons determine critical surgical
details like implant placement and leg alignment. By improving surgical accuracy and efficiency, this
research will support the partner organization in creating innovative tools that enhance patient outcomes
and open new opportunities for the company in the joint replacement market.

Faculty Supervisor:

Ilker Hacihaliloglu

Student:

Partner:

Torus Biomedical Solutions Inc.

Discipline:

Life Sciences

Sector:

Manufacturing

University:

The University of British Columbia

Program: