人工智能在人工晶状体屈光力计算的应用

Application of artificial intelligence in intraocular lens power calculation

目的:评估新一代基于人工智能(artificial intelligence,AI)的人工晶状体(intraocular lens,IOL)计算公式的准确性。方法:本研究为回顾性研究,纳入因白内障行晶状体超声乳化联合IOL植入术的262例患者262眼。在术前,通过IOLMaster700获取角膜曲率、角膜白到白、中央角膜厚度、前房深度、晶状体厚度以及眼轴长度。使用第三代公式(SRK/T、Holladay 1和Hoffer Q)、Barrett UniversalⅡ(BUⅡ)、新一代AI公式(Kane、Pearl-DGS、Hill-RBF 3.0、Hoffer QST和Jin-AI)对术后屈光状态进行计算,并与术后实际的屈光状态进行比较。在将预测误差(prediction error,PE)归零后,分析了各公式的标准差(standard deviation,SD)、绝对误差均值(mean absolute error,MAE)、绝对误差中位数(median absolute error,MedAE)以及PE在±0.25、±0.50、±1.00、±2.00 D范围内的百分比。结果:基于AI的IOL屈光力计算公式的SD、MAE和MedAE的范围分别为0.37 D(Kane和Jin-AI)至0.39 D(Hoffer QST)、0.28 D(Hill-RBF 3.0和Jin-AI)至0.31 D(Hoffer QST)以及0.21 D(Hill-RBF3.0和Jin-AI)至0.24 D(Hoffer QST);均低于第三代公式(SD:0.43 D~0.45 D;MAE:0.34 D;MedAE:0.25 D~0.28 D)。在所有公式中,Jin-AI公式预测误差在±0.50 D的比例最高,为84.73%,Kane(84.35%)和BUⅡ(83.97%)公式次之。结论:在IOL屈光力预测上,与传统第三代公式相比,新一代基于AI的公式表现出更高的准确性,可以使更多的患者在术后获得预期的屈光状态。

Objective:To evaluate the accuracy of new generation artificial intelligence(AI)-based intraocular lens(IOL)power calculation formulas.Methods:This retrospective study included a total of 262 eyes from 262 patients with cataract who underwent uneventful phacoemulsification combined with IOL implantation.Keratometry,corneal white-to-white,central corneal thickness,anterior chamber depth,lens thickness,and axial length were measured by the IOL Master 700 before surgery.Predicted refractive errors were calculated by the third-generation formulas(SRK/T,Holladay 1,and Hoffer Q),Barrett UniversalⅡ(BUⅡ),and the newer-generation AI formulas(Kane,Pearl-DGS,Hill-RBF 3.0,Hoffer QST,and Jin-AI),and were compared with the actual postoperative refractive value.After adjusting the prediction error(PE)to zero,the standard deviation(SD),mean absolute error(MAE),median absolute error(MedAE),and the percentage of a PE within the range of±0.25 diopter(D),±0.50 D,±1.00 D,and±2.00 D were analyzed.Results:The SD,MAE,and MedAE of the AI-based formulas ranged from 0.37 D(Kane and Jin-AI)to 0.39 D(Hoffer QST),0.28 D(Hill-RBF 3.0 and Jin-AI)to 0.31 D(Hoffer QST),and 0.21 D(Hill-RBF 3.0 and Jin-AI)to 0.24 D(Hoffer QST),respectively.These values were all lower than those of the third-generation formula(SD:0.43 D to 0.45 D;MAE:0.34 D;MedAE:0.25 D to 0.28 D).Among all the formulas,the Jin-AI formula had the highest proportion of a PE within±0.50 D(84.73%),followed by Kane(84.35%)and BUⅡ(83.97%)formulas.Conclusion:The new AI-based IOL formulas show higher accuracy compared with the traditional third-generation ones in predicting IOL power.thereby enabling more patients to achieve the expected refractive outcomes after surgery.