@inproceedings{dedd0493bb0d46669f5ca1e386074bef,
title = "Haptics-enabled surgical training system with guidance using deep learning",
abstract = "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.",
keywords = "Autoencoder, Deep learning algorithm, Haptic device, Machine learning, Motion tracking and quantification, Virtual surgical training system",
author = "Ehren Biglari and Marie Feng and John Quarles and Edward Sako and John Calhoon and Ronald Rodriguez and Yusheng Feng",
year = "2015",
doi = "10.1007/978-3-319-20684-4_26",
language = "English (US)",
isbn = "9783319206837",
volume = "9177",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "267--278",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "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 ; Conference date: 02-08-2015 Through 07-08-2015",
}