Arnaud Huaulmé1, Kanako Harada2, Germain Forestier3, Mamoru Mitsuishi2, Pierre Jannin4. 1. Univ Rennes, Inserm, LTSI - UMR_S 1099, 35000, Rennes, France. arnaud.huaulme@univ-rennes1.fr. 2. Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. 3. MIPS, University of Haute-Alsace, Mulhouse, France. 4. Univ Rennes, Inserm, LTSI - UMR_S 1099, 35000, Rennes, France.
Abstract
PURPOSE: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects. METHODS: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures. VALIDATION: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise. RESULTS: We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases. CONCLUSION: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.
PURPOSE: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects. METHODS: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures. VALIDATION: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise. RESULTS: We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases. CONCLUSION: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.
Authors: Fabien Despinoy; David Bouget; Germain Forestier; Cedric Penet; Nabil Zemiti; Philippe Poignet; Pierre Jannin Journal: IEEE Trans Biomed Eng Date: 2015-10-26 Impact factor: 4.538