仅用OCT对光眼进行分类:比较了三种来自机器学习的集群算法
Marc Biarnés1,2, Néstor Ventura-Abreu3,4,5, Ignacio Rodríguez-Una6
1Oftalmologia Mèdica i Quirúrgica (OMIQ) Research, Sant Cugat del Vallès, Spain.
Eye (London, England)
|October 19, 2023
概括
机器学习使用光谱域光学连贯性断层扫描 (SD-OCT) 数据准确地分类玻璃眼. 基于模型的聚类 (MBC) 在根据视网膜神经纤维层 (RNFL) 和状细胞内部状层 (GCIPL) 的测量来区分健康的眼睛和青光眼睛的准确性达到96.1%.
科学领域:
- 眼科和生物医学工程眼科和生物医学工程
- 医疗诊断中的人工智能
背景情况:
- 青光眼的诊断依赖于各种临床参数.
- 谱域光学连贯性断层扫描 (SD-OCT) 提供了详细的视网膜成像.
- 使用SD-OCT数据对玻璃眼的客观分类是积极研究的一个领域.
研究的目的:
- 为了客观地将眼睛分类为健康或眼,仅使用SD-OCT测量.
- 为了评估机器学习算法的有效性,特别是聚类方法,用于青光眼的检测.
- 为了利用周状视网膜神经纤维层 (pRNFL) 和状细胞内状层 (GCIPL) 数据进行分类.
主要方法:
- 三种集群算法 (k-means,层次集群分析[HCA]和基于模型的集群[MBC]) 应用于13个SD-OCT参数.
- 一个由109只眼睛组成的训练组被分为健康或眼.
- 性能最好的算法 (MBC) 在102只眼睛的独立测试集上得到了验证.
主要成果:
- 基于模型的集群 (MBC) 在培训样本中实现了最高的准确性 (91.7%).
- 在独立测试样本中,MBC显示了96.1%的整体准确度,94.3%的灵敏度和100%的特异性.
- 对于pRNFL测量的准确率为92.2%,对于GCIPL测量的准确率为98.0%.
结论:
- 聚类算法,特别是MBC,显示出使用SD-OCT数据对健康眼睛和眼睛进行区分的显著前景.
- 这种方法提供了一种客观的方法来评估仅基于结构测量的玻璃眼.
- 对这些方法的进一步了解可能会为绿眼病的病理生理学提供见解.
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