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通过使用深度学习方法的非糖尿病视网膜病变 fundus 图像检测糖尿病
Yovel Rom1, Rachelle Aviv1, Gal Yaakov Cohen2,3
1AEYE Health Inc., New York City, NY, USA.
Heliyon
|September 11, 2024
概括
人工智能现在只用眼底图像来检测糖尿病,即使没有视网膜病变. 这种非侵入性方法显示出在护理点早期糖尿病诊断的前景.
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:
- 糖尿病是全球主要的健康问题,导致严重的发病率和死亡率.
- 目前通过视网膜图像检测糖尿病依赖于识别视网膜病变,这是一个晚期并发症.
- 需要使用非侵入性,早期发现糖尿病的方法.
研究的目的:
- 开发和评估一个人工智能机器学习模型,用于使用 fundus 图像检测糖尿病.
- 评估模型能够独立于糖尿病眼病指标检测糖尿病的能力.
- 探索 fundus 图像用于非侵入性糖尿病诊断的潜力.
主要方法:
- 在大型EyePACS数据集 (47,076张图像) 上训练了一种机器学习算法.
- 该数据集包括按疾病持续时间和健康对照分层的患者队列.
- 使用接收器操作曲线 (AUC) 下的面积来评估性能.
主要成果:
- 人工智能模型实现了0.86的AUC,用于检测每次患者访问的糖尿病.
- 该模型显示每张图像检测糖尿病的AUC为0.83.
- 结果表明,仅通过 fundus 图像就能成功检测出糖尿病,而不论视网膜病变.
结论:
- 底部图像具有非侵入性糖尿病诊断的潜力.
- 这种人工智能驱动的方法可以促进照顾点和可访问的糖尿病查.
- 这项技术可能有助于诊断以前未被诊断的糖尿病患者.
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