基于机器学习和双眼视图像的眼外肌肉麻的自动诊断
Xiao-Lu Jin1, Xue-Mei Li1, Tie-Juan Liu1
1Ocular Motility Disorder Treatment Centre, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China.
International journal of ophthalmology
|May 19, 2025
概括
深度学习模型在通过双眼视图像诊断眼外肌肉麻时取得了高准确性,超过了其他机器学习算法. 这些先进的模型显示出作为临床诊断的可靠参考的前景.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 双眼视,或双视,可以是眼外肌肉麻的症状.
- 准确及时诊断眼外肌肉麻对于有效治疗至关重要.
- 计算机化双眼盲测试提供了一种标准化的数据收集方法.
研究的目的:
- 开发和评估机器学习模型,以使用双眼视图像和数据自动诊断眼外肌肉麻.
- 为了比较各种机器学习算法的诊断性能,包括深度学习.
主要方法:
- 追溯收集了来自3244个病例的双眼镜像,计算机化测试数据和患者记录.
- 使用后勤回归,决策树,支向量机,极端梯度增强和深度学习算法开发诊断模型.
- 使用测试集精度,混矩阵和精度回忆曲线对模型的评估.
主要成果:
- 深度学习模型 (64个类别和6个二进制分类) 实现了最高的测试集精度 (0.858).
- 与其他算法相比,深度学习模型表现出更高的加权精度 (0.85),回忆 (0.86),和F1得分 (0.85).
- 所有测试的机器学习算法都成功实现了眼外肌肉麻的自动诊断.
结论:
- 深度学习算法在眼外肌肉麻的诊断准确性方面显示出显著的优势.
- 深度学习模型的性能与医生提出的临床诊断一致.
- 这些机器学习模型可以作为诊断眼外肌肉麻的宝贵参考工具.
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