To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas. The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula. Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

中文翻译：

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患者组人工晶状体屈光度计算的机器学习适应

检查使用机器学习来调整患者组的人工晶状体 (IOL) 屈光度计算的有效性。在这项回顾性研究中，回顾和分析了在宫田眼科医院接受单焦点人工晶状体（SN60WF，Alcon）的 1,169 名日本患者的 1,611 只眼的临床记录。使用 769 名患者 1211 只眼的生物特征和术后屈光度数，优化了 SRK/T 和 Haigis 公式的常数。SRK/T 公式使用支持向量回归器进行调整。使用调整后的公式以及 SRK/T、Haigis、Hill-RBF 和 Barrett Universal II 公式的预测误差通过来自 395 名不同患者的 395 只眼睛的数据进行评估。平均预测误差、中值绝对误差和眼睛百分比在 ± 0.25 D、± 0.50 D 和 ± 1.00 D 内，并且在公式之间比较了超过 + 0.50 D 的误差。使用 SRT/K 和调整公式的平均预测误差小于使用其他公式 (P < 0.001)。在绝对误差方面，Hill-RBF 和适配方法优于其他方法。Barrett Universal II 的性能并不比其他患者组好。使用调整公式时远视屈光不正的眼睛最少（16.5%）。使用机器学习技术对来自特定患者组的数据进行调整 IOL 度数计算是有效且有前景的。Hill-RBF 和适应方法优于其他方法。Barrett Universal II 的性能并不比其他患者组好。使用调整公式时远视屈光不正的眼睛最少（16.5%）。使用机器学习技术对来自特定患者组的数据进行调整 IOL 度数计算是有效且有前景的。Hill-RBF 和适应方法优于其他方法。Barrett Universal II 的性能并不比其他患者组好。使用调整公式时远视屈光不正的眼睛最少（16.5%）。使用机器学习技术对来自特定患者组的数据进行调整 IOL 度数计算是有效且有前景的。