Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Department of Ophthalmology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Transl Vis Sci Technol. 2024 Oct 1;13(10):41. doi: 10.1167/tvst.13.10.41.
To quantitatively characterize the posterior morphology of high myopia eyes with posterior staphyloma.
Surface points of the eyeball were automatically extracted from magnetic resonance imaging scans using deep learning. Subsequently, the topography of posterior staphylomas was constructed to facilitate accurate visualization and quantification of their location and severity. In the three-dimensional Cartesian coordinate system established with surface points, measurements of distances (D) from each point to the hypothetical pre-elongation eye center within the eyeball and local curvatures (C) at each point on the posterior sclera were computed. Using this data, specific parameters were formulated. The concordance of these parameters with traditional staphyloma classification methods and their association with myopic traction maculopathy (MTM) grades based on the ATN classifications were investigated.
The study included 102 eyes from 52 participants. The measured parameters, particularly the variance of distance (Dvar) and the maximum value of the curvature and distance product (C · Dmax), demonstrated efficacy in differentiating various types of posterior staphyloma and exhibited strong correlations with the grades of MTM.
The automated generation of the posterior scleral topography facilitated visualization and quantification of staphyloma location and severity. Simple geometric parameters can quantify staphyloma shape and correlate well with retinal complications. Future works on expanding these measures to more imaging modalities could improve their clinical use and deepen insights into the link between posterior staphyloma and related retinal diseases.
This work has the potential to be translated into clinical practice, allowing for the accurate assessment of staphyloma severity and ultimately improving disease management.
定量描述伴有后葡萄肿的高度近视眼球的后部形态。
使用深度学习自动从磁共振成像扫描中提取眼球表面点。随后，构建后葡萄肿的地形，以方便准确地可视化和量化其位置和严重程度。在以表面点建立的三维笛卡尔坐标系中，计算了从每个点到眼球内假设的预伸长眼中心的距离（D）和后巩膜上每个点的局部曲率（C）。使用这些数据制定了特定的参数。研究了这些参数与传统葡萄肿分类方法的一致性及其与基于 ATN 分类的近视牵引性黄斑病变（MTM）分级的相关性。
该研究纳入了 52 名参与者的 102 只眼。所测量的参数，特别是距离方差（Dvar）和曲率与距离乘积的最大值（C·Dmax），在区分各种类型的后葡萄肿方面具有很好的效果，并且与 MTM 的分级有很强的相关性。
后巩膜地形的自动生成有助于可视化和量化葡萄肿的位置和严重程度。简单的几何参数可以量化葡萄肿的形状，与视网膜并发症有很好的相关性。未来将这些测量方法扩展到更多成像方式的研究可能会提高它们的临床应用价值，并深入了解后葡萄肿与相关视网膜疾病之间的联系。
翻译后的文本是对原文的准确翻译，不包含个人观点、解释或评价。
