基于深度学习的预测,在怀疑患有常态紧张型绿眼的患者中,对绿眼转换
Ahnul Ha1, Sukkyu Sun2, Young Kook Kim3,4
1Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of).
The British journal of ophthalmology
|November 2, 2023
概括
深度学习模型准确地预测了使用 fundus 图像和临床数据在怀疑患有青光眼 (GS) 的患者中正常张力青光眼 (NTG) 的转化. 这些模型可以预测正常眼内压的个体NTG发展的可能性和时间.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 怀疑是青光眼 (GS) 的正常眼内压 (IOP) 患者可以转换为正常压力青光眼 (NTG).
- 准确预测NTG转换对于及时干预至关重要.
- 当前的预测方法可能会从先进的计算方法中受益.
研究的目的:
- 评估深度学习 (DL) 模型在预测 NTG 转换方面的性能.
- 用 fundus 图像和临床数据评估 DL 的预测能力.
- 确定最佳的DL算法来预测GS患者NTG的发展.
主要方法:
- 利用了12458个GS眼睛的数据集,包括至少7年后的210只眼睛和IOP<21 mmHg.
- 使用卷积自编码器从光盘和无红色视网膜神经纤维层 (RNFL) 底部图像中提取特征.
- 采用XGBoost,随机森林和梯度提升分类器,其结合底部图像和15个临床特征 (例如年龄,IOP,RNFL厚度,血压).
主要成果:
- 这三种机器学习算法都显示出NTG转换预测的高诊断精度,AUC高达0.994.
- 在预测NTG转换的时间方面,XGBoost表现出卓越的性能 (平均平方误差为2.24).
- 转化时间的关键临床预测指标包括基线内血压,透气血压和平均环囊RNFL厚度.
结论:
- 集结基底图像和临床数据的深度学习模型显示,在正常血压GS患者中预测NTG转化具有显著的潜力.
- 这些模型可以预测到NTG转换的发生和时间.
- 这些发现支持使用人工智能驱动的工具来进行增强的玻璃眼风险评估.
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