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人工智能衍生了末气诱导的角膜损伤和不透明度等级
Rajnish Kumar1,2, Devansh M Sinha2, Nishant R Sinha1,2,3
1Harry S. Truman Memorial Veterans' Hospital, Columbia, MO, USA.
Scientific reports
|July 2, 2025
概括
人工智能 (AI) 模型现在可以客观地评估子中化学诱导的角膜损伤. 这种使用深度学习的自动化方法增强了临床前研究,以改善人类眼睛护理.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 毒理学 毒理学 毒理学
背景情况:
- 人工智能 (AI) 正在彻底改变眼科的疾病诊断和预后.
- 对化学损伤造成的角膜损伤的客观评估,特别是在临床前模型中,需要先进的工具.
- 目前用于分类化学诱导的角膜损伤的方法缺乏客观性和一致性.
研究的目的:
- 开发和验证人工智能衍生的临床分类模型,用于客观分级角膜损伤和不透明度水平.
- 评估AI在活中分析硫 (SM) 诱导的角膜损伤时的有效性.
- 建立一种用于分类角膜损伤的自动化方法,尽量减少临床前研究中的诊断错误.
主要方法:
- 利用401只子的角膜图像,诱导硫 (SM) 损伤,通过临床立体显微镜捕获.
- 使用的Mask-RCNN用于精确的角膜细分和提取.
- 应用深度学习模型,包括VGG16,ResNet101,DenseNet121,InceptionV3和ResNet50用于图像分类.
主要成果:
- 基于ResNet50的深度学习模型实现了87%的训练精度.
- 两个独立测试组的预测准确率分别为85%和83%.
- 结合的Mask-RCNN和ResNet50框架证明了可靠和统一的SM诱导的角膜损伤和不透明度的分级.
结论:
- 结合Mask-RCNN和ResNet50的深度学习框架提供了一种可靠的方法来对子中SM诱导的角膜损伤和不透明度进行分级.
- 这种人工智能模型提供了一个客观和自动化的方法来评估化学角膜损伤.
- 开发的模型增强了临床前研究的翻译应用,以改善人类眼睛护理.
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