Artificial intelligence Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty (AUTO-DALK)

项目总结 现代的眼科手术是由熟练的外科医生通过手术显微镜进行的， 利用徒手技术和手动操作的精密微型仪器，其结果往往是 受限于外科医生的技能水平和经验。为了克服这些人为因素，我们聚集在一起 一个跨学科的团队，包括一名临床科学家和眼科外科医生，一名光学设备科学家和 机器人工程师将我们实验室中现有的和正在开发的技术转化为精确度。 人工智能(AI)引导的精密自动化深前路眼科手术装置 板层角膜移植(Auto-DALK) DALK是治疗内皮功能正常的角膜疾病的一种极具吸引力的治疗方法。然而， 从技术角度来看，这一过程具有非同寻常的挑战性和耗时，限制了它的接受度 在角膜外科医生中。该手术最具挑战性的方面与基质的分层有关。 从Descemet膜(DM)中提取。一种通常被称为“大气泡”的程序被用来将基质从 DM采用深层间质内充气注射。然而，即使是有经验的外科医生也有困难 正在进行注射。DALK最常见的并发症是穿针过深 致Descemet膜穿孔需行全厚穿透性角膜移植 其恢复期要长得多，排斥反应导致移植物失败的风险也更高。据报道， 初学者Descemet膜穿孔占31.8%，经验丰富者占11.7%。 此外，供者和受者角膜之间的界面模糊是由 针插入深度不足，未能取出宿主间质组织，从而导致丢失 术后视力。这些问题直接关系到目前的外科实践无法 实时准确评估工具提示在角膜层内的深度。 在这里，我们将在我们之前和正在进行的强健光纤公共路径光纤工作的基础上再接再厉 基于卷积神经网络(CNN)机器人的相干层析成像(CP-OCT)和AI引导系统 使临床医生能够在微米级精确引导外科手术工具的显微外科工具。建议的自动- DALK手术工具系统能够进行一维实时深度跟踪、运动补偿和 早期检测器械与组织的接触，使临床医生能够准确、安全地进行DALK。 该工具将建立在一个手持平台上，该平台将由CP-OCT探头、环钻和微注射器组成， 允许精确和安全地切除直到DM的角膜前段 我们假设AI-OCT提供智能可视化和深度控制的最佳角膜 切割和组织跟踪将以比手动更高的准确性和效率执行DALK的任务 进行了环钻切割和“大气泡”肺分离术。

PROJECT SUMMARY 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.