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Recognition of Surgical Skills using Hidden Markov Models

Speidel, S.; Zentek, T.; Sudra, G.; Müller-Stich, B.P.; Gutt, C.; Dillmann, R.

Minimally invasive surgery is a highly complex medical discipline and can be regarded as a major breakthrough in surgical technique. A minimally invasive intervention requires enhanced motor skills to deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To recognize and analyze the current situation for context-aware assistance, we need intraoperative sensor data and a model of the intervention. Characteristics of a situation are the performed activity, the used instruments, the surgical objects and the anatomical structures. Important information about the surgical activity can be acquired by recognizing the surgical gesture performed. Surgical gestures in minimally invasive surgery like cutting, knot-tying or suturing are here referred to as surgical skills. We use the motion data from the endoscopic instruments to classify and analyze the performed skill and even use it for skill evaluation in a training scenario. The system uses Hidden Markov Models (HMM) to model and recognize a specific surgical skill like knot-tying or suturing with an average recognition rate of 92%.

Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2009
Sprache Englisch
Identifikator ISBN: 978-0-8194-7514-5
KITopen-ID: 1000027057
Erschienen in Medical Imaging 2009, Image perception, observer performance, and technology assessment 11-12 February 2009, Lake Buena Vista, Florida, United States. Ed.: S. Berkman
Verlag SPIE, Bellingham (Wash.)
Seiten Article ID 726125
Serie Proceedings of SPIE ; 7261
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