Haptics-enabled surgical training system with guidance using deep learning

Ehren Biglari; Marie Feng; John Quarles; Edward Sako; John Calhoon; Ronald Rodriguez; Yusheng Feng

doi:10.1007/978-3-319-20684-4_26

Haptics-enabled surgical training system with guidance using deep learning

Ehren Biglari, Marie Feng, John Quarles, Edward Sako, John Calhoon, Ronald Rodriguez, Yusheng Feng

Producción científica: Conference contribution

1 Cita (Scopus)

Resumen

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.

Idioma original	English (US)
Título de la publicación alojada	Universal Access in Human-Computer Interaction
Subtítulo de la publicación alojada	Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings
Editores	Constantine Stephanidis, Margherita Antona, Constantine Stephanidis
Editorial	Springer Verlag
Páginas	267-278
Número de páginas	12
ISBN (versión impresa)	9783319206837
DOI	https://doi.org/10.1007/978-3-319-20684-4_26
Estado	Published - 2015
Evento	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 - Los Angeles, United States Duración: ago 2 2015 → ago 7 2015

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	9177
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Other

Other	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
País/Territorio	United States
Ciudad	Los Angeles
Período	8/2/15 → 8/7/15

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-20684-4_26

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Biglari, E., Feng, M., Quarles, J., Sako, E., Calhoon, J., Rodriguez, R., & Feng, Y. (2015). Haptics-enabled surgical training system with guidance using deep learning. En C. Stephanidis, M. Antona, & C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction: Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings (pp. 267-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9177). Springer Verlag. https://doi.org/10.1007/978-3-319-20684-4_26

Haptics-enabled surgical training system with guidance using deep learning. / Biglari, Ehren; Feng, Marie; Quarles, John et al.
Universal Access in Human-Computer Interaction: Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings. ed. / Constantine Stephanidis; Margherita Antona; Constantine Stephanidis. Springer Verlag, 2015. p. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9177).

Producción científica: Conference contribution

Biglari, E, Feng, M, Quarles, J, Sako, E , Calhoon, J , Rodriguez, R & Feng, Y 2015, Haptics-enabled surgical training system with guidance using deep learning. En C Stephanidis, M Antona & C Stephanidis (eds.), Universal Access in Human-Computer Interaction: Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9177, Springer Verlag, pp. 267-278, 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, Los Angeles, United States, 8/2/15. https://doi.org/10.1007/978-3-319-20684-4_26

Biglari E, Feng M, Quarles J, Sako E , Calhoon J , Rodriguez R et al. Haptics-enabled surgical training system with guidance using deep learning. En Stephanidis C, Antona M, Stephanidis C, editores, Universal Access in Human-Computer Interaction: Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings. Springer Verlag. 2015. p. 267-278. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-20684-4_26

Biglari, Ehren ; Feng, Marie ; Quarles, John et al. / Haptics-enabled surgical training system with guidance using deep learning. Universal Access in Human-Computer Interaction: Access to Learning, Health and Well-Being - 9th International Conference, UAHCI 2015 Held as Part of HCI International 2015, Proceedings. editor / Constantine Stephanidis ; Margherita Antona ; Constantine Stephanidis. Springer Verlag, 2015. pp. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Haptics-enabled surgical training system with guidance using deep learning

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