İnam Maide Gözde, İnam Onur, Yang Xiangjun, Zeng Qun, Tezel Gülgün
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Department of Biophysics, Faculty of Medicine, Gazi University, Ankara, Turkey.
Curr Eye Res. 2025 May;50(5):502-511. doi: 10.1080/02713683.2025.2456783. Epub 2025 Jan 23.
This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups. The best neural network model was optimized for better outcomes. Prediction accuracy, the area under the curve, sensitivity, specificity, precision, and F-1 score values were obtained.
The Wilcoxon Signed-Rank test provided significantly higher validation accuracy for neural networks than decision trees, discriminant analysis, support vector machines, and k-nearest neighbor classifiers ( = 0.005 for all). For C57BL/6-DBA/2J classification, a mean validation accuracy of 88.11 ± 3.92% (95% CI: 86.99-89.22) was achieved for the neural network when the optimized neural network had 92.31% final test accuracy with an area under the curve value of 0.9762, 94.44% sensitivity, 90.48% specificity, 89.47% precision, and 0.92 F-1 score. The optimized neural network model for age group differentiation had a final test accuracy of 82.05% with a 0.9064 area under the curve value, 77.27% sensitivity, 88.24% specificity, 89.47% precision, and 0.83 F-1 score.
These findings validate that machine learning, using segmentation metrics instead of images, can effectively analyze retinal OCT scans in mice for categorical predictions in experimental models. Expanding this approach with additional features, including histopathological and functional correlations, is expected to improve the prediction power further, promising valuable applications to predict more complex outcomes in experimental and clinical studies.
本研究旨在初步测试机器学习方法能否使用视网膜分割指标对两个简单的生物学特征（小鼠年龄和小鼠物种）进行分类预测。
在对小鼠视网膜SD-OCT扫描进行分割后，对从C57BL/6和DBA/2J小鼠获得的视网膜层厚度数据进行机器学习处理。训练了22个模型来预测小鼠组。对最佳神经网络模型进行优化以获得更好的结果。获得了预测准确率、曲线下面积、灵敏度、特异性、精确率和F-1分数值。
Wilcoxon符号秩检验显示，神经网络的验证准确率显著高于决策树、判别分析、支持向量机和k近邻分类器（所有检验的P值均为0.005）。对于C57BL/6-DBA/2J分类，当优化后的神经网络最终测试准确率为92.31%，曲线下面积值为0.9762，灵敏度为94.44%，特异性为90.48%，精确率为89.47%，F-1分数为0.92时，神经网络的平均验证准确率为88.11±3.92%（95%CI：86.99-89.22）。用于年龄组区分的优化神经网络模型最终测试准确率为82.05%，曲线下面积值为0.9064，灵敏度为77.27%，特异性为88.24%，精确率为89.47%，F-1分数为0.83。
这些发现证实，使用分割指标而非图像的机器学习能够有效地分析小鼠视网膜OCT扫描，以在实验模型中进行分类预测。预计通过纳入更多特征（包括组织病理学和功能相关性）来扩展此方法，将进一步提高预测能力，有望在实验和临床研究中预测更复杂结果方面有重要应用。
