Yoshioka Nayuta, Zangerl Barbara, Nivison-Smith Lisa, Khuu Sieu K, Jones Bryan W, Pfeiffer Rebecca L, Marc Robert E, Kalloniatis Michael
Centre for Eye Health, University of New South Wales (UNSW), Sydney, New South Wales, Australia 2School of Optometry and Vision Science, UNSW, Sydney, New South Wales, Australia.
School of Optometry and Vision Science, UNSW, Sydney, New South Wales, Australia.
Invest Ophthalmol Vis Sci. 2017 Jun 1;58(7):3086-3099. doi: 10.1167/iovs.17-21450.
To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease.
Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20-85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort.
Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition-derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and -0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm).
Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.
描述黄斑神经节细胞层（GCL）随年龄的变化情况，并提供一个评估眼部疾病变化的框架。本研究使用数据聚类分析了一大群无眼部疾病受试者的光学相干断层扫描（OCT）黄斑GCL模式。
回顾性纳入在澳大利亚悉尼眼健康中心接受评估的201例患者的单眼（年龄范围20 - 85岁）；对从Spectralis OCT黄斑扫描获得的8×8网格位置进行无监督分类分析，分为具有共同GCL厚度且随年龄变化的统计学上可分离的类别。对所得类别和逐网格数据拟合线性和分段线性回归曲线。此外，分析归一化数据以确定回归百分比。通过将整个队列预测的50岁等效黄斑GCL厚度与真实的50岁参考队列进行比较，检验每个模型的准确性。
模式识别将黄斑区的GCL厚度聚类为五到八个空间上同心的类别。F检验表明分段线性回归是黄斑GCL变化最合适的模型。模式识别衍生模型和归一化模型显示，预测的黄斑GCL厚度与参考队列之间的差异（平均±标准差0.19±0.92和 - 0.30±0.61μm）小于逐网格模型（平均±标准差0.62±1.43μm）。
模式识别成功识别出随年龄呈分段线性减少的统计学上可分离的黄斑区域。该回归模型能更好地预测黄斑GCL厚度。模式识别揭示的各种独特空间模式与核心GCL厚度数据相结合，为分析眼部疾病中的GCL损失提供了一个框架。
