Recognizing Clinical Styles in a Dental Surgery Simulator.
Recognizing clinical style is essential for generating intelligent guidance in virtual reality simulators for dental skill acquisition. The aim of this study was to determine the potential of Dynamic Time Warping (DTW) in matching novices' tooth cutting sequences with those of experts. Forty dental students and four expert dentists were enrolled to perform access opening to the root canals with a simulator. Four experts performed in manners that differed widely in the tooth preparation sequence. Forty students were randomly allocated into four groups and were trained following each expert. DTW was performed between each student's sequence and all the expert sequences to determine the best match. Overall, the accuracy of the matching was high (95%). The current results suggest that the DTW is a useful technique to find the best matching expert for a student so that feedback based on that expert's performance can be given to the novice in clinical skill training.
School of Science and Technology, Bangkok University, Thailand.
Phattanapon
P
Rhienmora
Faculty of Information And Communication Technology, Mahidol University, Thailand.
Peter
P
Haddawy
Faculty of Dentistry, Thammasat University, Thailand.
Siriwan
S
Suebnukarn
School of Engineering and Technology, Asian Institute of Technology, Thailand.
Poonam
P
Shrestha
School of Engineering and Technology, Asian Institute of Technology, Thailand.
Matthew N
MN
Dailey
D000328:Adult; D000465:Algorithms; D002983:Clinical Competence; D003194:Computer-Assisted Instruction / Q000379:methods*; D003814:Dentistry, Operative / Q000193:education*; D004497:Education, Dental / Q000379:methods*; D004521:Educational Measurement / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D013647:Task Performance and Analysis; D013785:Thailand; D055815:Young Adult