Optical Coherence Tomography Guided Robotic Needle Insertion for Deep Anterior Lamellar Keratoplasty.

Journal Article (Journal Article)

OBJECTIVE: Deep anterior lamellar keratoplasty (DALK) significantly reduces the post-transplantation morbidity in patients eligible for partial-thickness cornea grafts. The popular "big bubble" technique for DALK is so challenging, however, that a significant fraction of corneal pneumodissection attempts fail for surgeons without extensive DALK-specific experience, even with previous-generation cross-sectional optical coherence tomography (OCT) guidance. We seek to develop robotic, volumetric OCT-guided technology capable of facilitating or automating the difficult needle insertion step in DALK. METHODS: Our system provides for real-time volumetric corneal imaging, segmentation, and tracking of the needle insertion to display feedback for surgeons and to generate needle insertion plans for robotic execution. We include a non-automatic mode for cooperative needle control for stabilization and tremor attenuation, and an automatic mode in which needle insertion plans are generated based on OCT tracking results and executed under surgeon hold-to-run control by the robot arm. We evaluated and compared freehand, volumetric OCT-guided, cooperative, and automatic needle insertion approaches in terms of perforation rate and final needle depth in an ex vivo human cornea model. RESULTS: Volumetric OCT visualization reduces cornea perforations and beneficially increases final needle depth in manual insertions by clinically significant amounts. Our automatic robotic needle insertion techniques meet or exceed surgeon performance in both needle placement and perforation rate. CONCLUSION: Volumetric OCT is a key enabler for surgeons, although robotic techniques can reliably replicate their performance. SIGNIFICANCE: Robotic needle control and volumetric OCT promise to improve outcomes in DALK.

Full Text

Duke Authors

Cited Authors

  • Draelos, M; Tang, G; Keller, B; Kuo, A; Hauser, K; Izatt, JA

Published Date

  • July 2020

Published In

Volume / Issue

  • 67 / 7

Start / End Page

  • 2073 - 2083

PubMed ID

  • 31751219

Pubmed Central ID

  • PMC7365552

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

Digital Object Identifier (DOI)

  • 10.1109/TBME.2019.2954505


  • eng

Conference Location

  • United States