一个通用和可解释的多标签多疾病查系统用于眼前段疾病检测
Mingyu Xu1,2, Lisha Wang3, Shengzhan Wang4
1Eye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Ophthalmology science
|September 2, 2025
概括
一个人工智能查系统有效地检测从裂纹灯图像中多个前段眼部疾病,显示出与眼科医生可比的高精度. 这项技术旨在改善初级眼科护理的可用性,并支持临床决策.
科学领域:
- 眼科 眼科
- 人工智能
- 医学成像
背景情况:
- 眼前部分疾病需要及时准确的诊断.
- 目前的选方法可能缺乏效率和广泛适用性.
- 开发自动化系统可以帮助早期发现和管理.
研究的目的:
- 开发和验证一个多标签,多疾病人工智能 (AI) 查系统.
- 应用该系统来检测常见的眼部前段疾病,使用切割灯图像.
- 确保该系统具有很好的概括性和可用于临床的解释性.
主要方法:
- 这是一项多中心的回顾性研究,涉及1990名患者和5132张裂灯图像.
- 在内部数据集上进行培训和验证,并对内部和外部数据集进行测试,包括受过较少培训的表型.
- 与眼科医生对比人工智能系统的性能.
主要成果:
- 人工智能系统在各种检测级别 (图像,区域,病变) 中实现了高精度,平均精度从0.827到0.972.
- 性能与眼科医生相比,即使在训练较少或新的表型上 (平均准确度为0. 852- 0. 950).
- 该系统表现出优异的多标签,多疾病检测和概括能力.
结论:
- 开发的AI查系统在检测多种眼前部位疾病方面表现强.
- 该系统表现出强大的概括性,即使训练数据的表型有限.
- 它准备提供可访问的初级医疗信息,并协助眼科医生进行临床实践.
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