电邮这篇文章 机器学习和人工神经网络技术在角膜切开术后畸形分类中的应用
- 作者: Tsyrenzhapova E.K.1, Rozanova O.I.1, Iureva T.N.1,2,3, Ivanov A.A.1, Rozanov I.S.4
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隶属关系:
- The S. Fyodorov Eye Microsurgery Federal State Institution
- Irkutsk State Medical University
- Russian Medical Academy of Continuous Professional Education
- LLC Transneft Technology
- 期: 卷 5, 编号 1 (2024)
- 页面: 64-74
- 栏目: 原创性科研成果
- URL: https://journals.rcsi.science/DD/article/view/262961
- DOI: https://doi.org/10.17816/DD624022
- ID: 262961
如何引用文章
详细
论证。对前放射状角膜切开术后患者角膜的光学和解剖特性进行仔细分析。这对于选择用于白内障手术和其他类型光学矫正的眼内镜片的光学倍率具有特殊意义。角膜切开术后畸形临床表现的多变性决定了有必要对其进行分类，这也是现代眼科学的一项重要任务。
目的。本研究旨在利用机器学习和人工神经网络开发角膜切开术后角膜畸形自动分类系统。该分类系统的开发基于对角膜图形数值的分析。
材料与方法。以250名患者的匿名病历分析结果为材料。患者年龄在46至76岁之间（平均年龄为59.63±5.95岁）。对500张角膜前后表面的图形，对角膜切开术后畸形分类进行了3个阶段的机器学习。
结果。第一阶段是分析角膜前后表面的图形。通过分析记录了角膜前后表面在三个环形区域的隆起数值。在第二阶段，通过深度机器学习选择并建立了一个前馈神经网络，确定了八个辅助参数。这些参数描述了角膜前后表面的形态。在第三阶段根据测试样本和训练样本的比例，获得了角膜切开术后角膜畸形的分类算法，该比例为75%至91%。
结论。开发了一个人工神经网络。成功解决了角膜切开术后角膜畸形类型的分类问题，准确率高达91%。该神经网络的训练质量还有进一步提高的潜力。人工神经网络算法的应用可以成为对曾接受过放射状角膜切开术的患者进行角膜切开术后角膜畸形自动分类的有用工具。
作者简介
Ekaterina K. Tsyrenzhapova
The S. Fyodorov Eye Microsurgery Federal State Institution
Email: katyakel@mail.ru
ORCID iD: 0000-0002-6804-8268
SPIN 代码: 1158-5233
MD
俄罗斯联邦, IrkutskOlga I. Rozanova
The S. Fyodorov Eye Microsurgery Federal State Institution
Email: olgrozanova@gmail.com
ORCID iD: 0000-0003-3139-2409
SPIN 代码: 6557-9123
MD, Dr. Sci. (Medicine)
俄罗斯联邦, IrkutskTatiana N. Iureva
The S. Fyodorov Eye Microsurgery Federal State Institution; Irkutsk State Medical University; Russian Medical Academy of Continuous Professional Education
Email: tnyurieva@mail.ru
ORCID iD: 0000-0003-0547-7521
SPIN 代码: 8457-5851
MD, Dr. Sci. (Medicine), Professor
俄罗斯联邦, Irkutsk; Irkutsk; IrkutskAndrey A. Ivanov
The S. Fyodorov Eye Microsurgery Federal State Institution
Email: ivanov.andrei.med@yandex.ru
ORCID iD: 0009-0001-4235-9252
MD
俄罗斯联邦, IrkutskIvan S. Rozanov
LLC Transneft Technology
编辑信件的主要联系方式.
Email: nauka@mntk.irkutsk.ru
ORCID iD: 0009-0001-7202-0428
俄罗斯联邦, Irkutsk
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