The formal assessment of surgical skills has grown in importance over recent years, with increasing evidence that unstructured systems of assessment have poor reproducibility, large inter-observer variation and lack of quantifiable measures. A paradigm shift has therefore begun, with the emergence of more objective and quantitative tools devised to complement current practice. In this work we aim to, through computer vision algorithms, determine if instruments can be tracked with sufficient accuracy during surgery so that the quantitative data obtained can be directly related to surgical performance, across a range of cataract types, surgeons, and equipment.

This will therefore potentially contribute to: feedback on dexterity performance during surgery; formative assessment of surgical performance; surgical training and through a combination of all of the above, improved patient safety. We present a robust algorithm based upon SURF point detection and optical flow that is capable of measuring instruments movement throughout the course of many operation procedures. In addition, the current experiments have shown that such measurements are able to separate different levels of surgeons based on their operation videos and estimate their surgical skills.