Johns Hopkins Receives NIH Grant for Artificial intelligence Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty (AUTO-DALK)
Johns Hopkins University Received a 2022 NIH Grant for $397,691 for Artificial intelligence Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty (AUTO-DALK). The principal investigator is Jin U. Kang. Below is a summary of the proposed work.
Contemporary ocular surgeries are performed by skilled surgeons through operating microscopes, utilizing freehand techniques and manually operated precision micro-instruments, where the outcomes are often limited by the surgeon's skill levels and experiences. To overcome these human factors, we have assembled an interdisciplinary team including a clinician-scientist and eye surgeon, an optical device scientist and medical robotic engineers to translate existing and developing technologies in our laboratories into precision, “deep- learning” artificial intelligence (AI) guided robotic ocular surgical devices for precise automated Deep Anterior Lamellar Keratoplasty (AUTO-DALK). DALK is a highly attractive treatment of corneal disease with normally functioning endothelium. However, the procedure is unusually challenging from a technical perspective and time-consuming, limiting its acceptance among corneal surgeons. The most challenging aspect of the procedure is related to the delamination of stroma from Descemet's membrane (DM). A procedure, commonly called “Big Bubble” is used to separate stroma from DM using deep intrastromal pneumatic injection. However, even experienced surgeons have difficulty precisely placing the injection. The most common complication of DALK is the excessive depth of the needle insertion resulting in Descemet's membrane perforation requiring conversion to full-thickness penetrating keratoplasty with its much longer recovery period and a higher risk of graft failure from rejection. The reported rates of Descemet's membrane perforation for beginner and experienced surgeons are 31.8% and 11.7% respectively. In addition, interface haze between the donor and recipient cornea is a common problem caused by the insufficient depth of needle insertion and failure to remove the host stromal tissue, which results in loss of postoperative visual acuity. These problems relate directly to the inability of the current surgical practice to precisely assess the depth of the tooltips inside the cornea layer in real-time. Here we will build upon our previous and ongoing work in robust fiber optic common-path optical coherence tomography (CP-OCT) and AI-guide system based on convolutional neural network (CNN) robotic microsurgical tools that enable clinicians to precisely guide surgical tools at micron scale. The proposed AUTO- DALK surgical tool system is capable of one-dimensional real-time depth tracking, motion compensation, and detection of early instrument contact with tissue, which enables clinicians to perform DALK precisely and safely. The tool will be built on a handheld platform that will consist of CP-OCT probe, trephine and microinjector that allows precise and safe removal of the anterior section of cornea down to DM We hypothesize that AI-OCT providing intelligent visualization and depth controlled optimal cornea cutting and tissue tracking will perform the task of DALK with better accuracy and efficiency over the manually performed trephine cutting and “Big Bubble” pneumodissection.