Background: Laparoscopic box trainer simulator has recently become a tool for assessment of physicians` surgical and laparoscopic skills, and training using such a simulator has been incorporated into the curricula of surgery syllabus. With the increased use of box trainer simulators, there is a great need for obtaining reliable and objective evaluations of the trainees` performances. In this article we introduce a novel, automated tool for assessing laparoscopic cutting performance by using image-processing algorithms.
Methods: Twenty-seven interns specializing in the fields of gynecology, urology and general surgery participated in 4-6 sessions, in which each trainee cut a circular patch positioned inside a low-cost homemade laparoscopic box trainer simulator. We developed a software to analyze the circle-cutout shapes and to produce a score for each cut.
Results: The trainees` performances and cutting accuracy were quantitatively measured using four parameters (standard deviation, area, skewness, and number of peaks). In each session, the software successfully generated a score for every trainee. Our scoring method showed that there is an improvement in cutting performance of novice trainees.
Conclusions: An objective scoring system based on various image processing algorithms was developed. Our method does not require an instructor or self-evaluation questionnaire. In a large set of trainees, our scoring method could give every trainee a measure of his/her performance relative to an average surgeon. Positive correlation of performance with experience needs to be checked on a larger scale.