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高精度和精确的自动化杯与盘的比例量化,用于眼查
Abadh K Chaurasia1, Connor J Greatbatch1, Xikun Han2,3
1Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia.
Ophthalmology science
|July 25, 2024
概括
一个新的深度学习算法准确地从 fundus 图像中确定杯与磁盘的比率 (CDR),有助于对青光眼的诊断. 这种自动化方法显示了图像分级性和CDR估计的高精度,改进了手动评估.
科学领域:
- 眼科和人工智能的人工智能
- 医学成像分析 医学成像分析
- 青光眼的诊断 青光眼的诊断
背景情况:
- 扩大的杯与光盘比率 (CDR) 是光眼神经病变的关键指标.
- 手动CDR评估可能是主观的,耗时的,并且不如自动化方法准确.
研究的目的:
- 开发和验证一个深度学习算法,用于从 fundus 图像进行自动 CDR 确定.
- 评估该算法在眼查中的实用性.
主要方法:
- 一个卷积神经网络 (CNN) 模型使用FastAI和PyTorch在来自英国生物银行 (UKBB) 的基底图像上进行了训练.
- 该模型是为图像分级分类和CDR估计 (回归分析) 开发的.
- 验证使用EyePACS和Drishti_GS.GS的多民族数据集进行.
主要成果:
- 该等级模型实现了97.13%的准确性,99.26%的精度和96.56%的AUC.
- 对于CDR估计,表现最好的回归模型实现了0.8514的确定系数.
- 在EyePACS数据集上进行的对外验证,对青光眼的分类产生了82.49%的准确性,72.02%的敏感性和82.83%的特异性.
结论:
- 开发的AI模型在图像分级性和CDR估计方面表现出高精度.
- 人工智能衍生的CDR估计是准确的,但对玻璃眼查的门需要考虑其他临床因素.
- 该算法显示了提高眼查效率和准确性的潜力.
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