高近视的机器学习方法:系统审查和元分析
Huiyi Zuo1, Baoyu Huang1, Jian He1
1Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China.
Journal of medical Internet research
|January 3, 2025
概括
机器学习 (ML) 在诊断病态近视和高近视方面表现出高准确度. 深度学习模型与传统的ML相比显示出更高的性能,为智能诊断工具提供了潜在的潜力.
科学领域:
- 眼科医生 眼科 眼科
- 医疗人工智能 医疗人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 机器学习 (ML) 在临床实践中越来越多地用于疾病诊断和预后.
- 眼科医学的ML应用侧重于诊断和预测高近视和相关疾病.
- 基于证据的验证对于眼科中基于ML的诊断工具至关重要.
研究的目的:
- 评估ML方法在检测高近视和病态近视方面的性能.
- 为开发和完善眼科智能诊断和预测工具提供证据.
- 评估ML在诊断高近视相关绿眼病中的准确性.
主要方法:
- 在PubMed,Cochrane,Embase和Web of Science的系统文献搜索到2023年9月.
- 使用双变的混合效应模型来评估诊断准确性的元分析.
- 基于ML目标和方法的偏差风险评估和子组分析.
主要成果:
- ML在诊断病态近视方面表现出很高的准确性 (SROC 0.97,灵敏度 0.91,特异性 0.95).
- 深度学习 (DL) 在诊断病态近视方面表现优于传统的ML.
- ML在诊断和预测高近视 (SROC 0.98) 和诊断高近视相关的玻璃眼 (SROC 0.96) 中表现出高准确度.
结论:
- ML在诊断高近视和病态近视方面表现出有希望的准确性.
- ML显示了预测未来高近视风险的潜力.
- DL是一种智能图像处理的可行方法,支持为服务不足的地区开发智能检查工具.
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