Master’s thesis · UiT The Arctic University of Norway
Automated Performance Analysis in Laparoscopic and Robotic Surgery
My current research asks a deceptively simple question: can software measure surgical skill? My thesis explores automated performance analysis in minimally invasive surgery — turning operative data into objective, useful feedback.
The problem
Laparoscopic and robotic surgery demand extraordinary precision, yet assessing a surgeon’s technical skill still relies heavily on subjective expert observation. That makes feedback slow, inconsistent, and hard to scale across training programs.
If we can analyse the data a procedure naturally produces — instrument motion, timing, and technique — we can build objective, repeatable measures of performance that support training and improve patient outcomes.
The approach
The thesis brings together my software background and computer-science research methods to design automated ways of measuring surgical performance — exploring what signals matter, how to extract them reliably, and how to present them in a way clinicians can trust.
Background & status
I began my master’s degree in computer science at UiT in 2005, studying part-time alongside my work as a developer and entrepreneur. The thesis is the culmination of that long-running effort, connecting two decades of building software with a research question that genuinely matters.
This page is a living summary — I’ll update it as the work progresses.