Department of Information Engineering, University of Padua, Italy.
Invest Ophthalmol Vis Sci. 2011 Aug 16;52(9):6404-8. doi: 10.1167/iovs.11-7529.
PURPOSE. An algorithm and a computer program for the automatic grading of corneal nerve tortuosity are proposed and evaluated. METHODS. Thirty images of the corneal subbasal nerve plexus with different grades of tortuosity were acquired with a scanning laser confocal microscope in normal and pathologic subjects. Nerves were automatically traced with an algorithm previously developed, and a tortuosity measure was computed with the proposed method, based on the number of changes in the curvature sign and on the amplitude (maximum distance of the curve from the underlying chord) of the nerve curves. These measures were evaluated according to their capability to reproduce the expert classification of images into three groups of tortuosity (low, mid, and high). This classification was also compared with measures provided by other methods proposed in the literature to evaluate nerve tortuosity. RESULTS. Among all considered methods, the one proposed herein allows a minimum of classification errors (only 2 in 30 images) and the highest Krippendorff concordance coefficient (0.96). Furthermore, it is the only one that can provide a significant difference (P < 0.01) between all pairs of tortuosity classes. CONCLUSIONS. The results provided by the proposed system confirmed its ability to perform a clinically significant evaluation of corneal nerve tortuosity.
目的。提出并评估了一种用于自动评估角膜神经扭曲度的算法和计算机程序。
方法。使用扫描激光共聚焦显微镜在正常和病理受试者中获取具有不同扭曲程度的 30 张角膜基底神经丛图像。使用先前开发的算法自动追踪神经，并使用所提出的方法基于曲率符号变化的数量和神经曲线的幅度（曲线与下方弦的最大距离）计算扭曲度度量。根据这些测量值将图像分类为低、中和高扭曲度三个组的能力来评估这些测量值。该分类还与文献中提出的评估神经扭曲度的其他方法提供的度量进行了比较。
结果。在所考虑的所有方法中，本文提出的方法允许的分类错误最少（仅 30 张图像中的 2 张），并且 Krippendorff 一致性系数最高（0.96）。此外，它是唯一能够在所有扭曲度类别之间提供显著差异（P < 0.01）的方法。
结论。所提出系统提供的结果证实了其对角膜神经扭曲度进行临床意义评估的能力。
