预测顶和后腔室尺寸 面膜眼内透镜使用硫对硫优化的人工智能技术
Xun Chen1, Yang Shen1, Yinjie Jiang1
1From the Fudan University Eye Ear Nose and Throat Hospital (X.C., Y.S., Y.J., M.C., Y.L., B.L., L.N., X.W., X.Z.); National Health Commission Key Lab of Myopia (Fudan University) (X.C., Y.S., Y.J., M.C., Y.L., B.L., L.N., X.W., X.Z.); Shanghai Research Center of Ophthalmology and Optometry (X.C., Y.S., Y.J., M.C., Y.L., B.L., L.N., X.W., X.Z.); Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (20DZ2255000) (X.C., Y.S., Y.J., M.C., Y.L., B.L., L.N., X.W., X.Z.).
American journal of ophthalmology
|July 5, 2023
概括
这项研究发现,使用硫与硫 (STS) 测量的人工智能模型显著改善了与传统白色对白色 (WTW) 测量相比,后腔眼内眼镜 (PIOL) 尺寸和顶的预测.
科学领域:
- 眼科医生 眼科 眼科
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 准确地预测后腔眼内眼镜 (PIOL) 顶和大小对于成功的折射手术至关重要.
- 传统方法通常依赖于白色对白色 (WTW) 测量,这可能无法完全捕捉前腔的解剖细微差别.
- 人工智能 (AI) 和大数据分析的进步为更精确的预测模型提供了潜力.
研究的目的:
- 评估AI驱动的PIOL金库和大小预测模型的准确性.
- 用STS测量优化模型的性能与传统基于WTW的模型进行比较.
主要方法:
- 一个追溯分析5873眼睛植入后腔PIOLs.
- 利用一种随机森林回归和分类模型,结合人工智能和大数据.
- 输入特征包括白色到白色 (WTW),水平STS和垂直STS测量;输出特征是手术后的金库和PIOL大小.
主要成果:
- 结合WTW,水平STS和垂直STS测量的人工智能模型在预测PIOL金库和尺寸方面表现出卓越的准确性,与单独WTW相比.
- 与传统的WTW模型相比,STS优化的模型对金库和PIOL大小预测的准确性显著提高 (P < .001).
- 使用结合的STS测量,PIOL尺寸的分类预测实现了高精度 (0.8170) 和曲线下的面积 (0.9540).
结论:
- 人工智能与STS优化相结合,显著提高了PIOL大小和库房预测的准确性.
- 通过STS测量优化的随机森林机器学习模型明显优于用于PIOL规划的传统WTW模型.
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