深度学习识别了高质量的 Fundus 照片,并提高了自动化初级开放角度玻璃眼瘤检测的准确性
Benton Chuter1, Justin Huynh1, Christopher Bowd1
1Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
Translational vision science & technology
|January 29, 2024
概括
一个深度学习 (DL) 模型准确地评估了 fundus 照片的质量. 过质量差的图像可以提高自动眼的检测准确度,减少手动审查的负担.
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:
- 从 fundus 照片中自动检测初级开放角光眼 (POAG) 对于早期诊断至关重要.
- 图像质量显著影响自动诊断模型的性能.
- 手动评估 fundus 照片质量是耗时且主观的.
研究的目的:
- 开发和评估一个深度学习 (DL) 模型来评估 fundus 照片质量.
- 量化基于DL的质量评估对自动化POAG检测准确性的影响.
主要方法:
- 一个DL质量模型在2815张来自DIGS/ADAGES的 fundus照片上受过训练,并在11350张来自OHTS的照片上进行了测试.
- 人类专家定义了高质量和低质量图像的基本真相.
- 用AUROC测量DL质量评估对DLPOAG检测的影响.
主要成果:
- 在区分高质量和低质量照片方面,DL质量模型获得了0.97的AUROC.
- 对于高质量的图像 (AUROC=0.87),DL POAG检测模型的准确性明显高于质量差的图像 (AUROC=0.77).
结论:
- 一个DL模型可以准确地评估底层照片的质量.
- 自动化质量评估提高了基于DL的POAG检测模型的诊断准确度.
- 整合DL质量评估简化了自动化 fundus 图像审查,并提高了诊断性能.
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