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7个基于人工智能的眼内透镜功率计算公式在极长的高加索眼睛中的精度

Wiktor Stopyra¹, Oleksiy Voytsekhivskyy², Andrzej Grzybowski³

¹From the MW-Med Eye Centre (W.S.), Krakow, Poland; Department of Medicine (W.S.), University of Applied Sciences, Nowy Targ, Poland.

American journal of ophthalmology

|November 13, 2024

概括

七个人工智能 (AI) 公式用于眼内透镜 (IOL) 功率计算,在长眼中进行了比较. 希尔-RBF 3.0显示出比卡尔莫纳更高的精度,而霍弗QST和希尔-RBF 3.0则超过了拉达斯超级方程式AI.

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:

精确的眼内镜 (IOL) 功率计算对于白内障手术后的视觉结果至关重要.
极长的眼睛 (轴长>30.00毫米) 对传统的IOL功率计算公式构成独特的挑战.
人工智能 (AI) 的进步为折射可预测性提供了潜在的改进.

研究的目的:

评估和比较七个基于人工智能的IOL功率计算公式在眼睛高近视和长轴长度的准确性.
为了确定最准确的AI公式来预测这个具有挑战性的患者队列中的折射结果.

主要方法:

一个回顾分析48只眼睛的轴长>30.00毫米,谁经历了无事件的化.
在手术前使用SRK/T,Holladay 2或Barrett Universal II.进行内腔镜功率计算.
手术后的折射误差是在手术后3个月评估的.
使用Hill-RBF 3.0,Kane,PEARL-DGS,拉达斯超级公式AI (LSF AI),霍弗QST,卡尔莫纳和朱卢公式来评估准确性.
关键指标包括根平均平方绝对误差 (RMSAE),中位数绝对误差 (MedAE) 和眼睛在±0.50 D预测误差内的百分比.

主要成果:

希尔-RBF 3.0显示了最低的RMSAE (0.788),在统计学上优于卡尔莫纳 (0.956).
与LSF AI (0.800) 相比,霍弗QST (0.442) 和Hill-RBF 3.0 (0.490) 的MedAE显著降低.
希尔-RBF 3.0,凯恩和霍弗QST在±0.50 D预测误差范围内实现了最高的眼睛百分比 (每个为54.17%),超过了LSF AI和卡尔莫纳.

结论:

所有的人工智能公式在极长的眼睛中都表现出相似的整体真实性.
根据RMSAE,Hill-RBF 3.0证明比卡尔莫纳更准确.
对于MedAE,LSF AI的准确性被发现低于霍弗QST和Hill-RBF 3.0的准确性.

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