Towards an Efficient Computational Framework for Surgical Skill Assessment: Suturing Task by Kinematic Data

Author(s):  
Parisa Hasani ◽  
Faraz Lotfi ◽  
Hamid D. Taghirad
Author(s):  
Lasitha Wijayarathne ◽  
Frank L. Hammond

Current surgical skill assessment methods are often based on the kinematics of manual surgical instruments during tool-tissue interactions. Though kinematic data are generally regarded as a sufficient basis for skill assessment, the inclusion of kinetic information would allow the assessment of measures such as “respect for tissue” and force control, which are also important aspects of surgical proficiency. Kinetic data would also provide a richer data set upon which automated surgical motion segmentation and classification algorithms can be developed. However, the kinetics of tool-tissue interactions are seldom included in assessments, due largely to the difficulty of mounting small sensors — typically silicon strain gauges — onto surgical instruments to capture force data. Electromagnetic (EM) or optical trackers used for kinematic measurement are often tethered, and thus having tethered force sensors also mounted on the same surgical instruments would complicate the experimental process and could affect/distort the acquired data by impeding the natural manual motions of surgeons. We present a surgical skill assessment platform which places the kinetic sensors in the environment, not on the instruments, to reduce the physical encumbrance of the system to the surgeon. This system can capture kinetic data using a standalone force/torque sensor embedded in a custom designed workspace platform, and kinematic data using EM trackers placed on the instruments. This portable platform enables the empirical characterization of open surgery motion trajectories and corresponding kinetic data without need for a centralized acquisition site, and will eventually be integrated into a completely untethered skill assessment system.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joël L. Lavanchy ◽  
Joel Zindel ◽  
Kadir Kirtac ◽  
Isabell Twick ◽  
Enes Hosgor ◽  
...  

AbstractSurgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.


2007 ◽  
Vol 6 (2) ◽  
pp. 188-191 ◽  
Author(s):  
Sharif Al-Ruzzeh ◽  
Shishir Karthik ◽  
David O'Regan

Ophthalmology ◽  
2011 ◽  
Vol 118 (2) ◽  
pp. 427-427.e5 ◽  
Author(s):  
Karl C. Golnik ◽  
Hilary Beaver ◽  
Vinod Gauba ◽  
Andrew G. Lee ◽  
Eduardo Mayorga ◽  
...  

2015 ◽  
Vol 72 (5) ◽  
pp. 910-917 ◽  
Author(s):  
Giovanni Saggio ◽  
Alessandra Lazzaro ◽  
Laura Sbernini ◽  
Francesco Maria Carrano ◽  
Davide Passi ◽  
...  

2017 ◽  
Vol 74 (2) ◽  
pp. 295-305 ◽  
Author(s):  
Ahmad Ghasemloonia ◽  
Yaser Maddahi ◽  
Kourosh Zareinia ◽  
Sanju Lama ◽  
Joseph C. Dort ◽  
...  

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