TY - JOUR
T1 - The minimally acceptable classification criterion for surgical skill
T2 - intent vectors and separability of raw motion data
AU - Dockter, Rodney L.
AU - Lendvay, Thomas S.
AU - Sweet, Robert M.
AU - Kowalewski, Timothy M.
N1 - Publisher Copyright:
© 2017, CARS.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Purpose: Minimally invasive surgery requires objective methods for skill evaluation and training. This work presents the minimally acceptable classification (MAC) criterion for computational surgery: Given an obvious novice and an obvious expert, a surgical skill evaluation classifier must yield 100% accuracy. We propose that a rigorous motion analysis algorithm must meet this minimal benchmark in order to justify its cost and use. Methods: We use this benchmark to investigate two concepts: First, how separable is raw, multidimensional dry laboratory laparoscopic motion data between obvious novices and obvious experts? We utilized information theoretic techniques to analytically address this. Second, we examined the use of intent vectors to classify surgical skill using three FLS tasks. Results: We found that raw motion data alone are not sufficient to classify skill level; however, the intent vector approach is successful in classifying surgical skill level for certain tasks according to the MAC criterion. For a pattern cutting task, this approach yields 100% accuracy in leave-one-user-out cross-validation. Conclusion: Compared to prior art, the intent vector approach provides a generalized method to assess laparoscopic surgical skill using basic motion segments and passes the MAC criterion for some but not all FLS tasks.
AB - Purpose: Minimally invasive surgery requires objective methods for skill evaluation and training. This work presents the minimally acceptable classification (MAC) criterion for computational surgery: Given an obvious novice and an obvious expert, a surgical skill evaluation classifier must yield 100% accuracy. We propose that a rigorous motion analysis algorithm must meet this minimal benchmark in order to justify its cost and use. Methods: We use this benchmark to investigate two concepts: First, how separable is raw, multidimensional dry laboratory laparoscopic motion data between obvious novices and obvious experts? We utilized information theoretic techniques to analytically address this. Second, we examined the use of intent vectors to classify surgical skill using three FLS tasks. Results: We found that raw motion data alone are not sufficient to classify skill level; however, the intent vector approach is successful in classifying surgical skill level for certain tasks according to the MAC criterion. For a pattern cutting task, this approach yields 100% accuracy in leave-one-user-out cross-validation. Conclusion: Compared to prior art, the intent vector approach provides a generalized method to assess laparoscopic surgical skill using basic motion segments and passes the MAC criterion for some but not all FLS tasks.
KW - Laparoscopic surgery
KW - Surgical motion
KW - Surgical skill evaluation
KW - Surgical training
UR - http://www.scopus.com/inward/record.url?scp=85019599515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019599515&partnerID=8YFLogxK
U2 - 10.1007/s11548-017-1610-9
DO - 10.1007/s11548-017-1610-9
M3 - Article
C2 - 28516302
AN - SCOPUS:85019599515
SN - 1861-6410
VL - 12
SP - 1151
EP - 1159
JO - International Journal of Computer Assisted Radiology and Surgery
JF - International Journal of Computer Assisted Radiology and Surgery
IS - 7
ER -