基于表格数据的青光眼诊断中机器学习的应用:系统性审查
Mohammad Hasan Shahriari1, Farkhondeh Asadi2, Hamid Moghaddasi3
1Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
BMC biomedical engineering
|August 1, 2025
概括
机器学习 (ML) 显示出对诊断青光眼的前景,这是导致失明的主要原因. 使用结构化数据,如光学连贯断层扫描 (OCT) 和视野 (VF) 测试,可以提高准确性,但在临床使用中需要提高数据质量.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 玻璃眼是全球不可逆转失明的主要原因.
- 目前青光眼的诊断方法缺乏客观性和一致性.
- 在眼科中,急需先进,可靠的诊断工具.
研究的目的:
- 系统地审查和评估机器学习 (ML) 技术在青光眼诊断中的有效性.
- 确定最有效的ML模型和数据类型,以提高青光眼的检测准确度.
- 评估临床青光眼诊断中ML应用的挑战和未来方向.
主要方法:
- 在五个主要数据库中进行了系统的文献审查.
- 根据预定义的纳入和排除标准,选出了35项相关研究.
- 分析重点是ML模型性能指标 (精度,AUC) 使用结构化数据 (OCT,VF,人口统计数据).
主要成果:
- 结构化数据,包括光学连贯断层扫描 (OCT),视野 (VF) 测试和人口信息,显著提高了诊断准确性.
- 机器学习模型,特别是支持矢量机器 (SVM),深度学习 (DL),随机森林和整体方法,实现精度从76%到98.3%.
- 这些模型的曲线下的面积 (AUC) 值在52.5%至99%之间,表明不同程度的诊断能力.
结论:
- 机器学习具有显著的潜力,可以提高青光眼诊断的准确性和可靠性.
- 诸如数据不平衡和有限的样本大小等挑战目前阻碍了ML模型的通用性.
- 进一步的研究,重点是数据质量改善和强大的模型验证,对于广泛的临床采用ML在眼的检测是必不可少的.
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