Alexandre Levy
ABSTRACT
Non-invasive robotized surgery is nowadays largely in action for most interventions because of its very beneficial advantages in terms of patient health and material efficiency. However, the still recurrent problem of guaranteeing the quality of suturing action (ie avoiding thread breaking) in all robotized interventions is recurrently impairing the overall results from this approach, mainly due to defective haptic information on threads available to the surgeons from the robot. To improve the efficiency of robot-surgeon collaboration, the problem of communicating relevant and reliable information on threads used by surgeons during suturing is addressed in present paper. From collected data on an experimental setup designed for the study described in Part I, machine learning predictive analysis is built-up in present Part. The approach helps understand the influence of different parameters on the suture ruptures and determine the safety zone in which the surgeon can pull the thread without danger. A display can be added to give the surgeon a visual return during the operations. Results obtained for different types of threads show up to 99% predictive accuracy, especially concerning maximum strength and maximum elongation of a suture before breaking.
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Index Terms
- An Innovative Approach to Safe Surgical Suturing Part II: Data Machine Learning Predictive Analysis
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