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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
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一个可解释的AI框架用于角膜成像解释和折射手术决策支持.

Mini Han Wang1,2,3

  • 1Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University), Zhuhai 519000, China.

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|November 27, 2025
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概括

本研究提出了一个使用神经符号推理和大语言模型 (LLM) 进行角膜分析的AI框架. 它准确地检测到早期的角质,并有助于折射手术决策,提供可解释的双语报告.

关键词:
角膜生物工程 角膜生物工程角膜的地形图是角膜的地形图.可解释的人工智能 (XAI)大型语言模型 (LLM)神经象征技术的神经象征技术手术决策支持手术决策支持

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 角膜拓分析对于诊断诸如角质等脱落性疾病以及规划折射手术至关重要.
  • 当前的解释方法可能是复杂和耗时的,需要先进的决策支持工具.

研究的目的:

  • 开发和验证可解释的神经符号和大语言模型 (LLM) 驱动的角膜地形解释框架.
  • 提供智能决策支持,用于早期检测角和折射手术规划.

主要方法:

  • 一个四个阶段的管道,包括自动参数提取,知识图映射,贝叶斯推理和基于LLM的报告 (DeepSeek,GPT-4.0).
  • 使用IOLMaster 700数据对20只眼睛进行前性分析.
  • 与高级角膜专家和基线卷积神经网络 (CNN) 和视觉转换器 (ViT) 模型进行比较.

主要成果:

  • 该框架在早期检测角瘤方面实现了高诊断性能 (92-94%的灵敏度/特异性,AUC为0.95).
  • 它在折射手术资格方面表现出强的表现 (F1得分为0.90).
  • 生成的双语报告在清晰度和临床实用性方面获得了很高的评分,输出生成速度快 (~95秒).

结论:

  • 神经符号和LLM驱动的框架为眼科实践提供了一个透明,快速和临床可行的AI解决方案.
  • 这种方法显著增强了早期的瘤疾病检测,并支持个性化的折射手术规划.
  • 可解释AI (XAI) 原则的整合确保了可靠和可解释的临床决策支持.