NDLI: Multivariate Autoregressive Modeling of Hand Kinematics for Laparoscopic Skills Assessment of Surgical Trainees

Content Provider	IEEE Xplore Digital Library
Author	Loukas, C. Georgiou, E.
Copyright Year	1964
Abstract	Virtual reality (VR) simulators aim to enhance surgical education by allowing trainees to optimize their skills without patient risk. To achieve this quality, an objective analysis of surgical dexterity is crucial. The application of hidden Markov models (HMMs) has offered important insights in the evaluation of surgical skills (e.g., task decomposition), but there are still issues that need standardization, especially when constructing the hand motion vocabulary. In this paper, we investigate an alternative approach based on multivariate autoregressive (MAR) models. Kinematic signals from orientation sensors attached to the instruments of a VR simulator were used to study the laparoscopic skills of surgical residents. Two different tasks were performed: knot tying and needle driving. A variational Bayesian (VB) approximation was employed to calculate the MAR coefficients, which after data reduction were fed to a classifier. The MAR weights also provided the opportunity to study the hand motion connections. Specificity (Spec) and sensitivity (Sens) analysis was used to evaluate and compare the classification performance between MAR models and HMMs. Our results demonstrate the strength of the proposed approach in recognizing surgical maneuvers of residents with limited experience in laparoscopic suturing. The MAR approach yielded the best performance (Sens/Spec: 86%-96%), significantly outperforming the well-established approach of statistical similarity between different HMMs (Sens/Spec: 64%-87%). Subjects at the end of residency training demonstrated more and greater hand motion couplings compared to beginners. The methodological aspects of the proposed approach may be easily embedded in the assessment module of modern laparoscopic simulators.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	9
File Size	854245
Starting Page	3289
Ending Page	3297
File Format	PDF
ISSN	00189294
Volume Number	58
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Surgery Laparoscopes Instruments Sensors Covariance matrix Training virtual reality (VR) simulator Hand kinematics laparoscopic surgery (LS) multivariate autoregressive (MAR) models surgical skill assessment
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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