关于计算机视觉在玻璃眼查中的使用临床观点
José Camara1, Antonio Cunha1,2
1Engineering Department, Universidade Tras-os-Montes (UTAD), 5000801 Vila Real, Portugal.
Medicina (Kaunas, Lithuania)
|March 28, 2024
概括
深度学习 (DL) 方法在自动化玻璃眼查方面表现有前途,为专家提供客观评估. 这项技术可以降低成本,提高青光眼光神经病变 (GON) 的诊断速度和一致性.
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:
- 玻璃眼是全球不可逆转失明的主要原因,需要早期检测.
- 目前对光眼神经病变 (GON) 的诊断方法面临挑战,原因是视神经头部呈现的变化,诊断界限的定义和专家主观性.
- 视网膜成像和功能评估方面取得了进展,但仍需要用于GON检测的客观参考.
研究的目的:
- 分析深度学习 (DL) 方法在青光眼查中的应用.
- 评估DL在降低GON评估成本和专业工作量方面的潜力.
- 为了提高青光眼诊断的速度和一致性.
主要方法:
- 利用深度学习 (DL) 方法来分析各种诊断数据,包括彩色底部照片 (CFP),视野 (VF) 测试和光学连贯性断层扫描 (OCT) 扫描.
- 基于DL的查与传统的临床评估进行了比较.
- 使用这些方法研究了GON特征的最低检测极限.
主要成果:
- 深度学习 (DL) 方法已经在帮助诊断和监测光眼神经病变 (GON) 的进展方面取得了成功.
- DL提供客观的分类,有可能减轻专家决策中的偏见.
- 使用DL进行的自动眼查产生了与临床现实密切一致的可靠结果.
结论:
- 深度学习 (DL) 方法是用于自动化玻璃眼查的有效工具.
- DL可以提高诊断青光斑视神经病变 (GON) 的客观性,速度和一致性.
- 在绿眼查中实施DL可以导致更可靠和更具成本效益的评估.
关键词:
早期显现的玻璃眼试验.通过照片进行青光眼查.通过远视眼科进行青光眼查.青光眼查成本 青光眼查成本预先测量玻璃眼查 (pre-perimetric glaucoma screening) 已经开始了.更多相关视频
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