TY - JOUR
T1 - Biomechanics-machine learning system for surgical gesture analysis and development of technologies for minimal access surgery
AU - Cavallo, Filippo
AU - Sinigaglia, Stefano
AU - Megali, Giuseppe
AU - Pietrabissa, Andrea
AU - Dario, Paolo
AU - Mosca, Franco
AU - Cuschieri, Alfred
PY - 2014/10/1
Y1 - 2014/10/1
N2 - Background. The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. Methods. The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. Results. The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. Conclusions. Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.
AB - Background. The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. Methods. The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. Results. The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. Conclusions. Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.
KW - biomechanical analysis of movement
KW - ergonomics
KW - machine learning approach
KW - metrics and benchmarks
KW - surgical gesture analysis
KW - surgical robotics
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U2 - 10.1177/1553350613510612
DO - 10.1177/1553350613510612
M3 - Article
C2 - 24297781
AN - SCOPUS:84907549588
VL - 21
SP - 504
EP - 512
JO - Surgical Innovation
JF - Surgical Innovation
SN - 1553-3506
IS - 5
ER -