高度近视眼传统与人工智能人工晶状体度数计算公式的比较评价。

IF 1.7 4区 医学 Q3 OPHTHALMOLOGY
Xiaopeng Jiang, Jiangjie Wang, Qingmin Jiang, Xiangyu Zhou, Fei Xia, Meng Gao
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引用次数: 0

摘要

目的:评价基于人工智能(AI)的人工晶状体(IOL)度数计算公式与传统方法在高度近视眼中的准确性,并评价其在不同眼轴长度和角膜曲率下的性能。方法:对115例高度近视眼行超声乳化人工晶状体植入术进行回顾性分析。采用4种常规公式(SRK/T、Haigis、Holladay 2、Barrett Universal II)和7种人工智能公式(Hill-RBF 3.0、Karmona、Hoffer QST、PEARL-DGS、Ladas Super Formula、Kane、HM-ZL)计算人工晶界。采用标准差(SD)评价结果,采用异方差检验;均方根绝对误差(RMSAE),用bootstrap-t方法评估;平均绝对误差(MAE),用Friedman检验评估;预测误差在±0.25 D至±1.00 D范围内的眼睛百分比,采用科克伦Q检验评估。根据眼轴长度(AL)和角膜曲率(Kmean)进行亚组分析。结果:大多数基于人工智能的公式(特别是Hill-RBF 3.0、PEARL-DGS和kane)比传统公式具有更高的准确性。结果:总体而言,Hill-RBF 3.0、PEARL-DGS和Kane的MAEs均显著低于Holladay 2 (P平均值)。结论:与传统方法相比,基于人工智能的屈光预测方法对高度近视患者具有更好的屈光预测效果。基于生物特征的量身定制的配方选择可能会提高白内障手术的屈光效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative evaluation of traditional and AI-based intraocular lens power calculation formulas in highly myopic eyes.

Comparative evaluation of traditional and AI-based intraocular lens power calculation formulas in highly myopic eyes.

Comparative evaluation of traditional and AI-based intraocular lens power calculation formulas in highly myopic eyes.

Comparative evaluation of traditional and AI-based intraocular lens power calculation formulas in highly myopic eyes.

Purpose: To assess the accuracy of artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas compared with traditional methods in highly myopic eyes, and to evaluate their performance across varying axial lengths and corneal curvatures.

Methods: This retrospective case series included 115 highly myopic eyes that underwent phacoemulsification with IOL implantation. IOL power was calculated using four conventional formulas (SRK/T, Haigis, Holladay 2, Barrett Universal II) and seven AI-based formulas (Hill-RBF 3.0, Karmona, Hoffer QST, PEARL-DGS, Ladas Super Formula, Kane, HM-ZL). The outcomes were evaluated using standard deviation (SD), assessed with Heteroscedastic test; root-mean-square absolute error (RMSAE), assessed with bootstrap-t method; mean absolute error (MAE), assessed with Friedman test; and the percentage of eyes within ± 0.25 D to ± 1.00 D of prediction error, assessed with Cochran's Q test. Subgroup analyses were performed based on axial length (AL) and corneal curvature (Kmean).

Results: Most AI-based formulas-especially Hill-RBF 3.0, PEARL-DGS and Kane-demonstrated higher accuracy than traditional formulas.

Results: Overall, the MAEs of Hill-RBF 3.0, PEARL-DGS, and Kane were significantly lower than that of Holladay 2 (P < 0.05). The SD of PEARL-DGS also differed significantly from Holladay 2 (P < 0.05). In the long axial length group, Hill-RBF 3.0, PEARL-DGS, and Kane showed significantly lower MAEs than Holladay 2 (P < 0.05). In the moderate corneal curvature group, BUⅡ, Hill-RBF 3.0, Hoffer QST, PEARL-DGS, and Kane had significantly lower MAEs than Holladay 2, and the SDs of Hill-RBF 3.0 and PEARL-DGS differed significantly from both Holladay 2 and SRK/T (P < 0.05). Trend lines showed that AI-based formulas exhibited more consistent and stable performance across different AL and Kmean.

Conclusion: AI-based formulas provide superior refractive prediction in highly myopic eyes compared with traditional methods. Tailored formula selection based on biometric profiles may enhance refractive outcomes in cataract surgery.

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来源期刊
BMC Ophthalmology
BMC Ophthalmology OPHTHALMOLOGY-
CiteScore
3.40
自引率
5.00%
发文量
441
审稿时长
6-12 weeks
期刊介绍: BMC Ophthalmology is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of eye disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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