Image analytics prediction of corneal keratoplasty failure

图像分析预测角膜移植术失败 总结 我们将创建专门的图像分析软件，用于预测角膜移植术（穿透性，内皮）失败- 从镜面反射角膜内皮细胞（EC）图像中获得。角膜成形术是最常见的 移植，失败率约为10%，导致失明，患者不适/焦虑，以及重复角膜炎， 与初始手术相比，整形手术失败的几率更高。通过成功的预测图像分析， 我们将能够识别有风险的移植角膜，并可能更积极地治疗它们， 局部皮质类固醇或其他措施，以防止失败。由于功能性内皮细胞（EC）层是 这对于维持透明角膜所必需的主动离子泵驱动的流体再分布是必要的，EC 已经分析了图像作为角膜健康的指标。正常EC层显示高细胞密度 排列成规则的六边形阵列。我们将利用现有的定量双- 来自EC图像的标记物（EC密度、细胞面积的变异系数和六边形）用于评估 角膜健康我们将计算与局部和远程细胞无序相关的其他图像特征， 与角膜移植排斥相关的成像属性以及来自计算机视觉的传统特征。包括这 这些特征的组合将为机器学习分类器提供丰富的输入，旨在预测未来的出口。 来（例如，失败或无失败）。我们将把方法应用到一个大的精心策划的数据集合， 凯斯西储大学（CWRU）的NIH资助的研究和Neth以前的研究， Erlands Institute for Innovative Ocular Surgery（NIIOS）我们的团队由图像处理专家，oph- 海洋学家，以及来自CWRU眼科和视觉科学系和大学的工作人员 医院（UH）眼科研究所的角膜图像分析阅读中心（CIARC），这是众所周知的严谨- 在大量的多学科研究中，对常规定量生物标志物进行了可靠的、高度可重复的评估， 机构临床试验。总之，我们的目标将是确定这种“第二代”EC分析是否能... 年龄可以预测角膜移植术失败。如果成功，该项目将导致软件， 翻译以支持研究和临床实践。

Image analytics for prediction of keratoplasty failure Summary We will create specialized image analytics software for prediction of keratoplasty (penetrating, endothelial) fail- ure from specular-reflection corneal endothelial cell (EC) images. Keratoplasties are the most common tissue transplant, with roughly a 10% failure rate, leading to blindness, patient discomfort/anxiety, and repeat kerato- plasties with a higher chance for failure than the initial procedure. With successful predictive image analytics, we will be in a position to identify transplanted corneas at risk and possibly treat them more aggressively with topical corticosteroids or other measures to prevent failure. Since a functional endothelial cell (EC) layer is necessary for the active ionic-pump-driven redistribution of fluid necessary to maintain the clear cornea, EC images have been analyzed as an indicator of cornea health. The normal EC layer exhibits high cell density arranged in a predominantly regular, hexagonal array. We will build on the use of existing quantitative bi- omarkers from EC images (EC density, coefficient of variation of cell areas, and hexagonality) used to evaluate cornea health. We will compute additional image features associated with local and long-range cell disarray, image attributes relevant to keratoplasty rejection, and traditional features from computer vision. Including this combination of features will provide rich inputs to machine-learning classifiers aimed at predicting future out- comes (e.g., failure or no failure). We will apply methods to a large aggregation of well-curated data from pre- vious NIH-funded studies at Case Western Reserve University (CWRU) and from previous studies at the Neth- erlands Institute for Innovative Ocular Surgery (NIIOS). Our team consists of image processing experts, oph- thalmologists, and staff from the CWRU Department of Ophthalmology and Visual Sciences and University Hospitals (UH) Eye Institute’s Cornea Image Analysis Reading Center (CIARC), which is well-known for rigor- ous, highly repeatable assessment of conventional quantitative biomarkers in a large number of multi- institutional clinical trials. Together, our goal will be to determine if this “second generation” analysis of EC im- ages can lead to prediction of keratoplasty failure. If successful, this project will lead to software which can be translated to support research and clinical practice.