神经眼科中的人工智能:诊断光学神经病变和视觉通路障碍的机会
Samendra Karkhur1, Arushi Beri1, Vidhya Verma1
1Ophthalmology, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Cureus
|September 16, 2025
概括
人工智能 (AI) 通过使用深度学习来改善视神经疾病的早期检测,从而增强神经眼科. 挑战包括数据的可变性和模型的透明度,需要对临床整合进行强有力的验证.
科学领域:
- 神经眼科神经眼科
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 神经眼科面临的挑战是复杂的诊断数据.
- 人工智能提供可扩展的解决方案,以提高诊断准确性和工作流程效率.
研究的目的:
- 探索人工智能在神经眼科中的变革性影响.
- 突出AI在诊断视神经和视觉通路障碍方面的作用.
主要方法:
- 深度学习 (DL) 和卷积神经网络 (CNN) 应用于 fundus 摄影,OCT 和 MRI.
- 对视野 (VF) 测试进行人工智能驱动的分析,用于监测疾病进展.
- 开发移动诊断应用程序和决策支持系统.
主要成果:
- 人工智能显示出在检测视神经炎,缺血性视神经病变,乳头发疹和玻璃眼等方面的潜力.
- 人工智能提高了对视野测试进展进行纵向监测的评估的一致性.
- 人工智能工具提高了在专业人员访问有限的领域的可访问性.
结论:
- 人工智能显著推进神经眼科诊断和监测.
- 解决数据异质性,模型解释性 (XAI) 和监管准则对于临床采用至关重要.
- 多中心验证和强有力的治理对于安全有效的AI实施至关重要.
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