Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery / by Loris Fichera
By: Fichera, Loris
Contributor(s): SpringerLink (Online service)
Material type:
E-bookSeries: Springer Theses, Recognizing Outstanding Ph.D. ResearchPublisher: Cham : Springer International Publishing, 2016Description: 1 recurso en línea (XIX, 99 p.) : 62 ilustraciones, 38 ilustraciones en color.ISBN: 9783319303307.Subject: Cirugía láser
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias de la Salud | RD73.L3 F53 2016 EB (Browse shelf(Opens below)) | .i11593015 | Acceso electrónico | eBOOK .i11593015 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
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| RD73 .L3 2020 EB Image-guided Laser Ablation | RD73.L3 ES Lasers in Surgery and Medicine | RD73 .L3 ES Lasers in Medical Science | RD73.L3 F53 2016 EB Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery | RD73.S785 2018 EB The SAGES Atlas of Robotic Surgery | RD73.S785 2018 EB The SAGES Manual of Robotic Surgery | RD73.S785 2019 EB The Route to Patient Safety in Robotic Surgery |
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{u2019} 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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