Towards a classification of surgical skills using affine velocity
- Author(s): Jenny Cifuentes 1 ; Minh Tu Pham 2 ; Pierre Boulanger 3 ; Richard Moreau 2 ; Flavio Prieto 4
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View affiliations
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Affiliations:
1:
Program of Electrical Engineering, Universidad de La Salle , Cra. 2 No. 10-70, Bogotá , Colombia ;
2: Department of Mechanical Engineering, INSA de Lyon , 20 Avenue Albert Einstein, Villeurbane , France ;
3: Department of Computer Science, University of Alberta , 116 Street, 85 Avenue, Edmonton , Canada ;
4: Department of Mechanical and Mechatronics Engineering, Universidad Nacional de Colombia , Carrera 45 No 26-85, Bogotá , Colombia
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Affiliations:
1:
Program of Electrical Engineering, Universidad de La Salle , Cra. 2 No. 10-70, Bogotá , Colombia ;
- Source:
Volume 12, Issue 4,
July
2018,
p.
548 – 553
DOI: 10.1049/iet-smt.2017.0373 , Print ISSN 1751-8822, Online ISSN 1751-8830
The aim of this study is to determine if navigation movements, used in surgical training, follow a particular power law which describes the relationship between the hand trajectory's curvature, torsion, and speed. Based on this approach, this study proposes the affine velocity as an appropriate classification feature to solve the surgical movement recognition problem. Eight subjects with different surgical experience were involved in the experiments. They were asked to do two kinds of movements that involve depth perception skills with their right arm. Using six video cameras and an instrumented laparoscope, the 3D trajectory of the end effector was recorded for each participant. A power law was used to fit the data sets and the exponents that relate the torsion, curvature, and speed were calculated. The exponents involved and the affine velocity for each trajectory were then computed, using a multi-variable linear regression, and compared between participants. It is shown that fitting residual follows a normal distribution indicating no regression biases. Finally, it is presented that an affine velocity analysis could be able to classify between both trajectories showing a correlation with the surgical skills and a clear difference for people with some surgical training.
Inspec keywords: medical image processing; image classification; video cameras; surgery; normal distribution; end effectors; regression analysis
Other keywords: surgical training; end effector; video camera; hand trajectory curvature; 3D trajectory; power law; normal distribution; instrumented laparoscope; depth perception skill; surgical movement recognition problem; surgical skill classification; affine velocity; torsion calculation; feature classification; speed calculation; curvature calculation; multivariable linear regression
Subjects: Probability theory, stochastic processes, and statistics; Other topics in statistics; Computer vision and image processing techniques; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Biomedical measurement and imaging; Image recognition; Other topics in statistics; Patient diagnostic methods and instrumentation; Patient care and treatment; Image sensors; Biology and medical computing; Patient care and treatment
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