背景：这项研究旨在评估放射状角膜切开术（RK）后眼睛的12种不同人工晶状体（IOL）屈光力计算公式的准确性。调查利用了地形/层析成像设备和基于人工智能（AI）的计算器的最新进展，将结果与现有文献报道的结果进行比较,以评估该患者组IOL计算的疗效和可预测性.
方法：在这项回顾性研究中，分析了在Hoopes视觉中心接受白内障手术的24例有RK病史的人的37只眼。术前进行生物测量和角膜地形图测量。术后6个月获得主观屈光。使用了12种不同的IOL功率计算，包括美国白内障和屈光手术协会（ASCRS）后RK在线配方，巴雷特真K,双K改装-霍拉迪1号，海吉斯-L，灵丹妙药,Camellin-Calossi,Emmetrypia验证光学(EVO)2.0,Kane,通过人工智能和输出线性化增强的预测-Debelemanière，Gatinel,和Saad(PEARL-DGS)公式。结果衡量标准包括绝对误差中位数(MedAE)，平均绝对误差(MAE)，算术平均误差(AME)，以及在±0.50D内实现屈光预测误差(RPE)的眼睛百分比，±0.75D,每个公式为±1D。还由两名独立的审阅者根据相关公式进行了文献搜索。
结果：总体而言，表现最好的IOL功率计算是Camellin-Calossi（MedAE=0.515D），ASCRS平均值(MedAE=0.535D)，以及基于EVO(MedAE=0.545D)和Kane(MedAE=0.555D)的AI公式。EVO和凯恩公式以及ASCRS计算类似地执行，48.65%的眼睛在目标范围的±0.50D内得分，而等效角膜测量读数（EKR）65Holladay公式在目标范围的±0.25D内获得了最大的眼睛评分百分比（35.14％）。此外,EVO2.0公式在±0.75DRPE类别内实现了64.86%的眼睛得分，而Kane公式在±1DRPE类别内获得了75.68％的眼睛得分。已建立的公式与新一代公式之间的MAE没有显着差异（P>0.05）。与ASCRS平均值和其他高性能公式相比，Panacea公式始终表现不佳（P<0.05）。
结论：这项研究证明了基于AI的IOL计算公式的潜力，比如EVO2.0和凯恩，用于提高白内障手术后RK眼IOL功率计算的准确性。既定的计算，例如ASCRS和BarrettTrueK公式，保持有效的选择，虽然使用不足的公式，比如EKR65和Camellin-Calossi公式，显示承诺，强调需要进一步研究和更大规模的研究来验证和增强该患者组的IOL功率计算。
BACKGROUND: This
study aims to evaluate the accuracy of 12 different intraocular lens (IOL) power calculation formulas for post-radial keratotomy (RK) eyes. The investigation utilizes recent advances in topography/tomography devices and artificial intelligence (AI)-based calculators, comparing the results to those reported in current literature to assess the efficacy and predictability of IOL calculations for this patient group.
METHODS: In this retrospective
study, 37 eyes from 24 individuals with a history of RK who underwent cataract surgery at Hoopes Vision Center were analyzed. Biometry and corneal topography measurements were taken preoperatively. Subjective refraction was obtained 6 months postoperatively. Twelve different IOL power calculations were used, including the American Society of Cataract and Refractive Surgery (ASCRS) post-RK online formula, and the Barrett True K, Double K modified-Holladay 1, Haigis-L, Panacea, Camellin-Calossi, Emmetropia Verifying Optical (EVO) 2.0, Kane, and Prediction Enhanced by Artificial Intelligence and output Linearization-Debellemanière, Gatinel, and Saad (PEARL-DGS) formulas. Outcome measures included median absolute error (MedAE), mean absolute error (MAE), arithmetic mean error (AME), and percentage of eyes achieving refractive prediction errors (RPE) within ± 0.50 D, ± 0.75 D, and ± 1 D for each formula. A search of the literature was also performed by two independent reviewers based on relevant formulas.
RESULTS: Overall, the best performing IOL power calculations were the Camellin-Calossi (MedAE = 0.515 D), the ASCRS average (MedAE = 0.535 D), and the EVO (MedAE = 0.545 D) and Kane (MedAE = 0.555 D) AI-based formulas. The EVO and Kane formulas along with the ASCRS calculation performed similarly, with 48.65% of eyes scoring within ± 0.50 D of the target range, while the Equivalent Keratometry Reading (EKR) 65 Holladay formula achieved the greatest percentage of eyes scoring within ± 0.25 D of the target range (35.14%). Additionally, the EVO 2.0 formula achieved 64.86% of eyes scoring within the ± 0.75 D RPE category, while the Kane formula achieved 75.68% of eyes scoring within the ± 1 D RPE category. There was no significant difference in MAE between the established and newer generation formulas (P > 0.05). The Panacea formula consistently underperformed when compared to the ASCRS average and other high-performing formulas (P < 0.05).
CONCLUSIONS: This
study demonstrates the potential of AI-based IOL calculation formulas, such as EVO 2.0 and Kane, for improving the accuracy of IOL power calculation in post-RK eyes undergoing cataract surgery. Established calculations, such as the ASCRS and Barrett True K formula, remain effective options, while under-utilized formulas, like the EKR65 and Camellin-Calossi formulas, show promise, emphasizing the need for further research and larger studies to validate and enhance IOL power calculation for this patient group.
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