Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery [recurso electrónico] / by Loris Fichera.
Tipo de material: TextoSeries Springer Theses, Recognizing Outstanding Ph.D. ResearchEditor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: XIX, 99 p. 62 illus., 38 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319303307Tema(s): Engineering | Minimally invasive surgery | User interfaces (Computer systems) | Robotics | Automation | Biomedical engineering | Engineering | Biomedical Engineering | Robotics and Automation | User Interfaces and Human Computer Interaction | Minimally Invasive SurgeryFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 610.28 Clasificación LoC:R856-857Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
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.