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

人工智能光学相干断层扫描引导深前板层角膜移植术（AUTO-DALK）

• 批准号: 10556431
• 负责人: Jin U Kang
• 金额: $ 41万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10556431
• 关键词: Accounting; Address; Adrenal Cortex Hormones; Animal Model; Anterior; Artificial Intelligence; Blindness; Blunt Trauma; Burr hole procedure; Cadaver; Clinical; Compensation; Complication; Consumption; Cornea; Corneal Diseases; Corneal Opacity; Corneal dystrophy; Data; Descemet' s membrane; Devices; Discipline; Distal; Drops; Early Diagnosis; Endophthalmitis; Endothelial Cells; Endothelium; Engineering; Ensure; Epithelium; Excision; Expert Systems; Eye; Eye Surgeon; Failure; Fiber Optics; Geometry; Glaucoma; Goals; Graft Survival; Hemorrhage; Human; Image; Immune; Incidence; Infection; Injections; Intelligence; Intraoperative Complications; Iris; Keratoconus; Keratoplasty; Laboratories; Lamellar Keratoplasty; Manuals; Medical; Microscope; Microsurgery; Modeling; Motion; Movement; Needles; Ocular Hypertension; Operative Surgical Procedures; Ophthalmology; Optical Coherence Tomography; Optics; Oryctolagus cuniculus; Outcome; Pathological Dilatation; Patients; Penetrating Keratoplasty; Perforation; Performance; Postoperative Complications; Postoperative Period; Procedures; Ptosis; Recovery; Repeat Surgery; Reporting; Research Personnel; Risk; Robotics; Rupture; Safety; Scientist; Secondary to; Structure; Surgeon; Surgical complication; System; Systems Development; Techniques; Technology; Testing; Thick; Time; Tissues; Topical Corticosteroids; Translating; Transplantation Surgery; Trauma; Validation; Visual; Visual Acuity; Visualization; Work; convolutional neural network; corneal scar; curative treatments; deep learning; design; empowerment; experience; graft failure; high risk; iatrogenic injury; improved; in vivo; instrument; interest; mechanical drive; microrobot; novel; optical fiber; phantom model; photonics; preservation; prototype; sensor; skills; surgery outcome; tool

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.

项目概要 当代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，其结果通常是 受外科医生技术水平和经验的限制，为了克服这些人为因素，我们集合了。 一个跨学科团队，包括临床医生科学家和眼外科医生、光学设备科学家和医学专家 机器人工程师将我们实验室中现有和正在开发的技术转化为精确的、“深层次的” “学习”人工智能（AI）引导的机器人眼科手术设备，用于精确的自动化深前部手术 板层角膜移植术（AUTO-DALK）。 DALK 是一种非常有吸引力的治疗内皮功能正常的角膜疾病的方法。 从技术角度来看，该程序异常具有挑战性且耗时，限制了其接受度 在角膜外科医生中，该手术最具挑战性的方面与基质分层有关。 使用通常称为“大气泡”的程序将基质与后弹力层分离。 使用深层基质内气动注射进行 DM 然而，即使是经验丰富的外科医生也很难精确地进行注射。 DALK 最常见的并发症是进针深度过深。 导致后弹力层穿孔，需要转换为全层穿透性角膜移植术 其恢复期更长，并且因排斥反应而导致移植失败的风险更高。 初学者和经验丰富的外科医生的后弹力层穿孔率分别为 31.8% 和 11.7%。 此外，供体角膜和受体角膜之间的界面混浊是由角膜移植引起的常见问题。 针插入深度不足且未能去除宿主基质组织，从而导致损失 这些问题与目前的手术实践无法实现术后视力直接相关。 实时精确评估工具提示在角膜层内的深度。 在这里，我们将建立在我们之前和正在进行的稳健光纤共路径光学方面的工作基础上 基于卷积神经网络（CNN）机器人的相干断层扫描（CP-OCT）和AI引导系统 显微手术工具使步兵能够在微米级精确引导手术工具。 DALK手术工具系统能够进行一维实时深度跟踪、运动补偿和 检测仪器与组织的早期接触，使 Strong 能够精确、安全地执行 DALK。 该工具将构建在一个手持式平台上，该平台由 CP-OCT 探头、环钻和显微注射器组成， 允许精确、安全地去除角膜前部直至 DM 我们捕捉到 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好的准确性和效率执行 DALK 任务 进行了环钻切割和“大气泡”气解剖。