图像协调和深度学习的自动分类加病在早产的视网膜病变的早产
Ananya Subramaniam1, Faruk Orge2, Michael Douglass1
1Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Journal of medical imaging (Bellingham, Wash.)
|October 5, 2023
概括
人工智能现在可以使用智能手机图像检测早产视网膜病变中的"加病". 这项技术提高了船只的可见性,为早期诊断和治疗这种令人失明的疾病提供了低成本的解决方案.
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:
- 过早性视网膜病 (ROP) 是早产婴儿的关键视网膜血管疾病,可能导致失明.
- 早期检测"加病",一个严重的ROP阶段,对于及时治疗至关重要.
- 目前的ROP监控依赖于昂贵的设备,限制了在低资源环境中的访问.
研究的目的:
- 开发一种人工智能驱动的方法,使用智能手机的基底图像在ROP中检测"加疾病".
- 通过移动技术使低资源环境中可访问的ROP选成为可能.
主要方法:
- 智能手机摄像头和廉价镜头被用来获得 fundus 图像.
- 一个预处理管道提高了视网膜血管的可见性和协调图像.
- 一个深度学习分类器 (GoogLeNet) 被训练来识别"加疾病"与没有"加疾病".
主要成果:
- 预处理显著提高了90%的容器对比度.
- 儿科眼科医生证实在预处理图像中,血管可见性得到改善.
- 人工智能模型实现了0.9754的ROC曲线下的区域,用于"加疾病"检测.
结论:
- 人工智能对智能手机图像的分析显示,对于分期ROP"加疾病"的高准确性.
- 这种方法为ROP选提供了一个有希望的,低成本的替代方案.
- 进一步开发算法和软件可以促进广泛的临床使用.
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