A machine learning approach to understand surgical performance

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)445-447
Number of pages3
JournalInternational journal of computer assisted radiology and surgery
Volume1
Issue numberSUPPL. 7
DOIs
Publication statusPublished - Jun 2006

Fingerprint

Hidden Markov models
Learning systems
Human engineering
Simulators
Exercise
Experiments
Machine Learning
Surgeons

Keywords

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Transplantation

Cite this

A machine learning approach to understand surgical performance. / Sinigaglia, S.; Megali, G.; Cavallo, F.; Tonet, O.; Dario, P.; Pietrabissa, A.

In: International journal of computer assisted radiology and surgery, Vol. 1, No. SUPPL. 7, 06.2006, p. 445-447.

Research output: Contribution to journalArticle

Sinigaglia, S. ; Megali, G. ; Cavallo, F. ; Tonet, O. ; Dario, P. ; Pietrabissa, A. / A machine learning approach to understand surgical performance. In: International journal of computer assisted radiology and surgery. 2006 ; Vol. 1, No. SUPPL. 7. pp. 445-447.
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