Duke University Receives NIH Grant for Intrasurgical OCT Image-Guided Robot Assist Device for Partial Thickness Corneal Transplantation
Duke University Receives a 2019 NIH Grant for $150,000 for Intrasurgical OCT Image-Guided Robot Assist Device for Partial Thickness Corneal Transplantation. The principal investigator is Joseph Izatt. The program began in 2019 and ends in 2021. Below is a summary of the proposed work.
This is an application for an Exploratory/Developmental Research Grant which brings together a team of experts in ophthalmic imaging, surgery and robotics to advance the state of the art in image-guided robotic microsurgical intervention. Corneal transplants are one of the most commonly performed allograft procedures worldwide, although there remains a substantial risk of immune rejection or graft failure. An alternative form of corneal transplantation known as Deep Anterior Lamellar Keratoplasty (DALK) has been described which solves most of the drawbacks of conventional corneal transplantation. However, this procedure requires great manual dexterity and experience because depth is difficult to assess using the standard ophthalmic surgical microscope. Even in experienced hands, DALK fails more often than not, requiring conversion to the older PKP procedure. Over the past decade, our team has become a leading academic research group applying optical coherence tomography (OCT) technology in ophthalmic microsurgery. Our current surgical microscope- integrated OCT (MIOCT) systems provide real time, in vivo microscopic imaging of the surgical field, viewed by the surgeon via a stereoscopic heads-up display or stereoscopic monitor. In the proposed project, we will leverage this state-of-the-art imaging technology to develop a robotic surgical assist device to enable microscale maneuverability to match the microscale visualization afforded by OCT. In the envisioned robotic assist device, the surgeon cooperatively guides an ergonomic tissue dissector handpiece comprising the robot arm end effector to a corneal insertion site, whereupon novel device control software will generate an optimal needle insertion trajectory using a cornea soft-tissue deformation model. Following surgeon approval of the plan, the device will automatically execute the needle trajectory. At all times, the control software will monitor needle position under MIOCT visualization (also visible to the surgeon), compensate for surgeon hand tremor, prevent penetration of the corneal endothelium, and allow for surgeon modification or termination of the procedure. The end result of this project will be a proof of concept of the component and integrated technologies required for robot-assisted DALK surgery, suitable for advancement to first-in-human trials in a subsequent project. The expected outcome of this proposal is a suite of technologies with the potential to significantly enhance ophthalmic microsurgical technique by overcoming challenges of surgical tool visualization and manipulation on the micrometer scale. These technologies will facilitate surgeon training, advance the skills of experienced surgeons, and thus potentially improve surgical outcomes for patients. Further, we believe that development and demonstration of these technologies forms a foundation for transfer to other microsurgical applications, such as neurosurgery, hand surgery, and others that may benefit from live microscopic 3D guidance using OCT as well as potentially other imaging techniques