Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery

Loris Fichera

ISBN 10: 3319807811 / ISBN 13: 9783319807812

Edité par Springer, Berlin, Springer International Publishing, Athena Research Centre, Springer, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783319807812

Détails bibliographiques

• Titre: Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery
• Éditeur: Springer, Berlin, Springer International Publishing, Athena Research Centre, Springer
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319807811
• ISBN à 13 chiffres: 9783319807812
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 6 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 6 millimètres de profondeur
• Poids: 195 grammes

A propos de ce titre

Synopsis

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.