基于机器学习的早期年龄相关性黄斑变性诊断  

Early Diagnosis of Age-Related Macular Degeneration Based on Machine Learning

作　　者：李浩宇 

机构地区：[1]温州大学计算机与人工智能学院,浙江 温州

出　　处：《计算机科学与应用》2025年第2期94-101,共8页Computer Science and Application

摘　　要：本研究聚焦于利用机器学习区分早期年龄相关性黄斑变性(AMD)与正常对照组。鉴于AMD致盲率高且患病率随老龄化上升,早期检测至关重要。采用包含数千张图像的公开数据集,筛选出早期AMD患者和正常对照组的视网膜OCT图像,经基于U-net网络分割为9层后,利用Python的Mathotas包计算各层前13个Haralick纹理特征值,并通过Kolmogorov-Smirnov检验及相应t检验或Mann-WhitneyU检验筛选特征。统计分析显示ONL、MEZ、RPE层纹理特征在两组间差异显著,OS层差异较小。模型分类中,LightGBM和XGBoost性能优于逻辑回归和SVM,前两者在MEZ、ONL层AUC值高,后两者在OS层表现差。研究为早期AMD诊断提供参考,但OS层问题有待进一步研究改进。This study focuses on using machine learning to distinguish early age-related macular degeneration (AMD) from normal control groups. Given the high rate of blindness caused by AMD and its increasing prevalence with aging, early detection is crucial. Using a public dataset containing thousands of images, retinal OCT images of early AMD patients and normal controls were selected. These images were segmented into 9 layers using a U-net based network. The first 13 Haralick texture features of each layer were calculated using Python’s Mahotas package, and features were selected through Kolmogorov-Smirnov tests and corresponding t-tests or Mann-Whitney U tests. Statistical analysis showed significant differences in texture features of the ONL, MEZ, and RPE layers between the two groups, with smaller differences in the OS layer. In model classification, LightGBM and XGBoost outperformed logistic regression and SVM, with the former two showing high AUC values in the MEZ and ONL layers, while the latter two performed poorly in the OS layer. The study provides a reference for early AMD diagnosis, but issues with the OS layer require further research and improvement.

关 键 词：年龄相关黄斑变性(AMD) Haralick纹理特征 机器学习 外核层(ONL) 视网膜色素上皮细胞层(RPE) 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]