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 | |
| Publication status | Published - Jun 2006 |
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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 journal › Article
}
TY - JOUR
T1 - A machine learning approach to understand surgical performance
AU - Sinigaglia, S.
AU - Megali, G.
AU - Cavallo, F.
AU - Tonet, O.
AU - Dario, P.
AU - Pietrabissa, A.
PY - 2006/6
Y1 - 2006/6
N2 - 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.
AB - 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.
KW - Hidden Markov Model
KW - Minimally invasive surgery
KW - Performance evaluation
KW - Surgical simulator
KW - Surgical training
UR - http://www.scopus.com/inward/record.url?scp=33748973934&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33748973934&partnerID=8YFLogxK
U2 - 10.1007/s11548-006-0032-x
DO - 10.1007/s11548-006-0032-x
M3 - Article
AN - SCOPUS:33748973934
VL - 1
SP - 445
EP - 447
JO - Computer-Assisted Radiology and Surgery
JF - Computer-Assisted Radiology and Surgery
SN - 1861-6410
IS - SUPPL. 7
ER -