Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Master of Engineering Science


Electrical and Computer Engineering


Ladak, Hanif M.

2nd Supervisor

Agrawal, Sumit K.



Mastoidectomy is a difficult surgery to master and requires significant training. The primary goal of this work was to implement real-time performance metrics for an existing virtual surgical training simulator. To achieve this goal, image segmentation and bone- thickness estimation software modules were first interfaced to the system to provide patient-specific training data to the metrics system. Nine performance metrics were developed: burr selection, burr visibility, burr stroke consistency, burr orientation, excessive force application, dissected volume, drilling in proximity to critical structures, injury to critical structures, and critical structure exposure. Additionally, functionality was provided to view computed metrics, replay the virtual procedure, or re-attempt a portion of the procedure. Even with the metrics included, the simulator was able to maintain interactive performance. Construct validity of the metrics was established by demonstrating that they show a difference between junior and expert groups. Automated performance metrics may permit independent practice without requiring an instructing surgeon.

Summary for Lay Audience

Surgeries that are performed near the ear are difficult to learn because there are many small, complex, and fragile structures within that area. Additionally, the structures in this area vary greatly from patient to patient, making it difficult to understand. If structures within this area are not understood well by the surgeon there is a great risk of causing life-changing injury to the patient. Traditionally, surgeons train for this by shadowing an expert surgeon or using a cadaver. However, modern computer software can allow a trainee to practice inner ear procedures using virtual reality and a computer simulator. Advanced computer programs can even accept a patient’s medical scan and convert it into a three-dimensional simulation. In this way, trainees may gain additional practice without the need for a real-world patient or cadaver. This work seeks to improve virtual ear surgery applications by adding an automatic evaluator. In this way, the computer program would understand a trainee's surgical ability and notify them of proper and improper decisions as they perform the virtual training procedure.

Available for download on Sunday, December 31, 2023