TY - GEN
T1 - Haptics-enabled surgical training system with guidance using deep learning
AU - Biglari, Ehren
AU - Feng, Marie
AU - Quarles, John
AU - Sako, Edward
AU - Calhoon, John
AU - Rodriguez, Ronald
AU - Feng, Yusheng
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In this paper, we present a haptics-enabled surgical training system integrated with deep learning for characterization of particular procedures of experienced surgeons to guide medical residents-in-training with quantifiable patterns. The prototype of virtual reality surgical system is built for open-heart surgery with specific steps and biopsy operation. Two abstract surgical scenarios are designed to emulate incision and biopsy surgical procedures. Using deep learning algorithm (autoencoder), the two surgical procedures were trained and characterized. Results show that a vector with 30 real-valued components can quantify both surgical patterns. These values can be used to compare how a resident- in-training performs differently as opposed to an experienced surgeon so that quantifiable corrective training guidance can be provided.
AB - In this paper, we present a haptics-enabled surgical training system integrated with deep learning for characterization of particular procedures of experienced surgeons to guide medical residents-in-training with quantifiable patterns. The prototype of virtual reality surgical system is built for open-heart surgery with specific steps and biopsy operation. Two abstract surgical scenarios are designed to emulate incision and biopsy surgical procedures. Using deep learning algorithm (autoencoder), the two surgical procedures were trained and characterized. Results show that a vector with 30 real-valued components can quantify both surgical patterns. These values can be used to compare how a resident- in-training performs differently as opposed to an experienced surgeon so that quantifiable corrective training guidance can be provided.
KW - Autoencoder
KW - Deep learning algorithm
KW - Haptic device
KW - Machine learning
KW - Motion tracking and quantification
KW - Virtual surgical training system
UR - http://www.scopus.com/inward/record.url?scp=84947290947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84947290947&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20684-4_26
DO - 10.1007/978-3-319-20684-4_26
M3 - Conference contribution
AN - SCOPUS:84947290947
SN - 9783319206837
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 278
BT - Universal Access in Human-Computer Interaction
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Stephanidis, Constantine
PB - Springer Verlag
T2 - 9th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015
Y2 - 2 August 2015 through 7 August 2015
ER -