脑膜炎对图像分级性和使用手持摄像头自动化糖尿病视网膜病变查的影响. 一个现实世界的设置评估评估
Iago Diógenes Azevedo Costa1, David Restrepo2,3, Lucas Zago Ribeiro1
1Ophthalmology Department, Escola Paulista de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Arquivos brasileiros de oftalmologia
|February 4, 2026
概括
脑膜炎或瞳孔扩张显著改善了糖尿病视网膜病变查的图像分级性和AI性能. 在资源有限的环境中,优化AI用于非水图像至关重要.
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:
- 在低收入和中等收入国家,糖尿病视网膜病变查面临挑战,原因是获得专门护理的机会有限.
- 便携式视网膜摄像机是一种可行的替代方案,但图像质量受到瞳孔膨胀 (心水病) 的影响,影响AI性能.
研究的目的:
- 为了评估肌肉病对图像分级性和基于AI的糖尿病视网膜病变检测的影响.
- 评估AI模型在现实世界中的性能,资源有限的设置与不同的肌痛病状态.
主要方法:
- 在mydriatic和非mydriatic组之间比较可分级图像比例.
- 使用通用估计方程来识别影响等级性的因素.
- 在移动眼科数据集上训练并验证了一个ResNet-200dAI模型,评估性能指标.
主要成果:
- 与非水图像 (55.6%) 相比,水图像的分级率更高 (82.1%).
- 人工智能模型的性能优于中水图像 (精度85.15%,AUC 0.94) 与非中水图像 (精度79.68%,AUC 0.93) 相比.
- 在非心水图像中降低分级性与高血压,年龄较大,男性性别和糖尿病持续时间较长有关.
结论:
- 脑膜炎显著提高了糖尿病视网膜病变查的图像分级性和AI性能.
- 优化人工智能校准和对非水分图像的值对于在药物扩张可能不切实际的低资源环境中有效实施至关重要.
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