Data-Driven Modelling and Control for Robot Needle Insertion in Deep Anterior Lamellar Keratoplasty.
Medical robots and systems
model learning for control
surgical robotics: planning
Journal
IEEE robotics and automation letters
ISSN: 2377-3766
Titre abrégé: IEEE Robot Autom Lett
Pays: United States
ID NLM: 101680812
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
medline:
24
4
2023
pubmed:
24
4
2023
entrez:
24
04
2023
Statut:
ppublish
Résumé
Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation which is associated with reduced patient morbidity. DALK has been explored as a potential application of robot microsurgery because the small scales, fine control requirements, and difficulty of visualization make it very challenging for human surgeons to perform. We address the problem of modelling the small scale interactions between the surgical tool and the cornea tissue to improve the accuracy of needle insertion, since accurate placement within 5% of target depth has been associated with more reliable clinical outcomes. We develop a data-driven autoregressive dynamic model of the tool-tissue interaction and a model predictive controller to guide robot needle insertion. In an
Identifiants
pubmed: 37090091
doi: 10.1109/lra.2022.3140458
pmc: PMC10117280
mid: NIHMS1772628
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1526-1533Subventions
Organisme : NEI NIH HHS
ID : R21 EY029877
Pays : United States
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