Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery.

IEEE Int Conf Robot Autom

Aided Medical Procedures & Augmented Reality, Technical University of Munich, 85748 Munich, Germany; Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.

Published: May 2024

AI Article Synopsis

  • Recent advancements in treatments for age-related macular degeneration require precise, minimally invasive delivery methods to avoid trauma to vital eye structures.
  • Skilled surgeons face challenges due to hand tremors, prompting the development of robotic systems that utilize fiber Bragg grating (FBG) sensors to monitor forces during surgery.
  • This paper introduces a specialized dataset combining force feedback data with optical coherence tomography (OCT) images and presents a neural network model for estimating forces based on these images, highlighting the dataset's potential use.

Article Abstract

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch's membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical interventions. This has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force data. We introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset's applicability.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11501085PMC
http://dx.doi.org/10.1109/icra57147.2024.10610807DOI Listing

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