Cataract surgical skill assessment tools

2014 ◽  
Vol 40 (4) ◽  
pp. 657-665 ◽  
Author(s):  
Sidharth Puri ◽  
Shameema Sikder
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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