Mori Yosai, Yamauchi Tomofusa, Tokuda Shota, Minami Keiichiro, Tabuchi Hitoshi, Miyata Kazunori
Miyata Eye Hospital, 6-3 Kurahara-cho, Miyakonojo, Miyazaki, 885-0051, Japan.
Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji, Hyogo, 671-1227, Japan.
Eye Vis (Lond). 2021 Nov 15;8(1):42. doi: 10.1186/s40662-021-00265-z.
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.
研究使用机器学习来调整患者群体的人工晶状体（IOL）屈光度计算的有效性。
在这项回顾性研究中，对在宫田眼科医院接受单焦点IOL单一型号（SN60WF，爱尔康）的1169名日本患者的1611只眼睛的临床记录进行了回顾和分析。使用769名患者的1211只眼睛的生物测量指标和术后验光结果，对SRK/T公式和海吉斯公式的常数进行了优化。使用支持向量回归器对SRK/T公式进行了调整。使用395名不同患者的395只眼睛的数据，评估了调整后公式以及SRK/T、海吉斯、希尔-径向基函数（Hill-RBF）和巴雷特通用II公式的预测误差。比较了各公式之间的平均预测误差、中位数绝对误差以及误差在±0.25 D、±0.50 D和±1.00 D范围内以及误差超过+0.50 D的眼睛百分比。
使用SRT/K公式和调整后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，希尔-径向基函数方法和调整后的方法优于其他方法。对于该患者群体，巴雷特通用II公式的性能并不优于其他公式。使用调整后公式时，远视屈光不正的眼睛最少（16.5%）。
使用来自特定患者群体的数据，通过机器学习技术调整人工晶状体屈光度计算是有效且有前景的。
