使用基于各种眼科设备组合的机器学习预测手术后的顶和最佳的植入式结合器镜片大小
Xi Chen1, Yiming Ye1, Huan Yao1
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Biomedical engineering online
|June 15, 2023
概括
人工智能 (AI) 模型可以使用多个眼科设备的数据准确地预测可植入合镜头 (ICL) 的密室和尺寸. 超声波生物显微镜 (UBM) 测量特别有效地提高了ICL手术的安全性和精度.
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:
- 植入式膜透镜 (ICL) 手术是有效的近视纠正.
- 预测ICL金库和大小仍然是一个挑战.
- 人工智能在眼科中的应用正在扩大.
研究的目的:
- 开发人工智能模型来预测手术后的体和ICL大小.
- 为了比较不同的AI算法和数据组合.
- 为了确定最佳的眼科设备和参数,用于ICL预测.
主要方法:
- 追溯研究1941年的眼睛.
- 利用人工智能算法和堆叠集体学习.
- 对比了Pentacam,Sirius和超声波生物显微镜 (UBM) 的数据.
主要成果:
- 联合Pentacam,Sirius和UBM显示出最好的预测准确度.
- 超声波生物显微镜的Sulcus-to-Sulcus (STS) 测量表现优于白色对白色 (WTW).
- 通过双设备组合或单设备参数,包括单独的UBM,可以实现有效的预测.
结论:
- 使用多个眼科设备的AI策略增强了ICL密室预测和大小.
- 在改善ICL手术的安全性和准确性方面,UBM测量 (STS) 是至关重要的.
- 这些发现突显了UBM在优化ICL植入结果方面的潜力.
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