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Cognitive supervision for robot-assi...
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Fichera, Loris.
Cognitive supervision for robot-assisted minimally invasive laser surgery[electronic resource] /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
杜威分類號:
617.058
書名/作者:
Cognitive supervision for robot-assisted minimally invasive laser surgery/ by Loris Fichera.
作者:
Fichera, Loris.
出版者:
Cham : : Springer International Publishing :, 2016.
面頁冊數:
xix, 99 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:
Springer eBooks
標題:
Lasers in surgery.
標題:
Surgical robots.
標題:
Engineering.
標題:
Biomedical Engineering.
標題:
Robotics and Automation.
標題:
User Interfaces and Human Computer Interaction.
標題:
Minimally Invasive Surgery.
ISBN:
9783319303307
ISBN:
9783319303291
內容註:
Introduction -- Background: Laser Technology and Applications to Clinical Surgery -- Cognitive Supervision for Transoral Laser Microsurgery -- Learning the Temperature Dynamics During Thermal Laser Ablation -- Modeling the Laser Ablation Process -- Realization of a Cognitive Supervisory System for Laser Microsurgery -- Conclusions and Future Research Directions.
摘要、提要註:
This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons' perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures.
電子資源:
http://dx.doi.org/10.1007/978-3-319-30330-7
Cognitive supervision for robot-assisted minimally invasive laser surgery[electronic resource] /
Fichera, Loris.
Cognitive supervision for robot-assisted minimally invasive laser surgery
[electronic resource] /by Loris Fichera. - Cham :Springer International Publishing :2016. - xix, 99 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..
Introduction -- Background: Laser Technology and Applications to Clinical Surgery -- Cognitive Supervision for Transoral Laser Microsurgery -- Learning the Temperature Dynamics During Thermal Laser Ablation -- Modeling the Laser Ablation Process -- Realization of a Cognitive Supervisory System for Laser Microsurgery -- Conclusions and Future Research Directions.
Open access.
This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons' perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures.
ISBN: 9783319303307
Standard No.: 10.1007/978-3-319-30330-7doiSubjects--Topical Terms:
487036
Lasers in surgery.
LC Class. No.: RD73.L3
Dewey Class. No.: 617.058
Cognitive supervision for robot-assisted minimally invasive laser surgery[electronic resource] /
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This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons' perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures.
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