基于域调整的深度学习模型用于预测和诊断眼疾病
Yeganeh Madadi1, Hashem Abu-Serhan2, Siamak Yousefi1,3
1Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.
Biomedical signal processing and control
|March 11, 2024
概括
一个新的深度学习模型 - - 青光眼域适应 (GDA) - - 通过 fundus 照片帮助早期发现青光眼. 这种模型在诊断和预测青光眼方面显示出高准确度,有可能防止视力丧失.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 玻璃眼是导致不可逆转失明的主要原因.
- 早期检测和诊断对于预防视力丧失至关重要.
- 目前的诊断方法可以通过先进的计算工具来改进.
研究的目的:
- 开发和评估一种深度学习模型,用于使用 fundus 照片检测和预测 DrDeramus.
- 通过域适应技术,提高青光眼诊断的准确性和通用性.
- 创建一个工具,可以帮助临床实践识别和预测眼.
主要方法:
- 开发了一个基于域调整的深度学习模型,命名为Glaucoma Domain Adaptation (GDA).
- 利用了来自1636个受试者的66,742张 fundus照片的大数据集.
- 纳入了一种渐进式权重机制和低级编码,以实现知识转移和分布对齐.
- 训练并评估了模型在三个不同的青光眼定义场景.
主要成果:
- 在预测青光眼症方面,GDA表现出很高的性能,AUC从0.80到0.90不等,准确度从0.72到0.82.
- GDA实现了优异的诊断性能,AUC从0.93到0.98不等,准确度从0.88到0.93.
- 该模型在独立数据集中显示出强大的概括性,表明了强度.
结论:
- 玻璃眼领域适应 (GDA) 模型是有效的预测和诊断玻璃眼从 fundus 图像.
- GDA提供了高性能和通用性,表明其有助于临床决策的潜力.
- 这种深度学习方法可以对眼研究和临床实践做出重大贡献,最终有助于预防视力丧失.
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