A machine learning approach to understand surgical performance

S. Sinigaglia; G. Megali; F. Cavallo; O. Tonet; P. Dario; A. Pietrabissa

doi:10.1007/s11548-006-0032-x

A machine learning approach to understand surgical performance

S. Sinigaglia, G. Megali, F. Cavallo, O. Tonet, P. Dario, A. Pietrabissa

IRCCS Fondazione Policlinico San Matteo

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper the Hidden Markov Models (HMMs) are used as a tool for understanding minimally invasive surgical performance and human factors that characterize it In our experiments we studied data concerning the tools positioning during exercises performed on a surgical simulator. By means of Hidden Markov Models theory, we created a model of the "expert surgeon performance" able to evaluate surgical capability and to distinguish expert and novice surgeon performances. By analyzing the trained model and video acquisitions, we show that it is possible to deduce information about features characterizing the surgical expertise.

Original language	English
Pages (from-to)	445-447
Number of pages	3
Journal	International journal of computer assisted radiology and surgery
Volume	1
Issue number	SUPPL. 7
DOIs	https://doi.org/10.1007/s11548-006-0032-x
Publication status	Published - Jun 2006

Keywords

Hidden Markov Model
Minimally invasive surgery
Performance evaluation
Surgical simulator
Surgical training

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Transplantation

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AU - Megali, G.

AU - Cavallo, F.

AU - Tonet, O.

AU - Dario, P.

AU - Pietrabissa, A.

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KW - Performance evaluation

KW - Surgical simulator

KW - Surgical training

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