Turull-Mallofré Aina, Aldaba Mikel, Pujol Jaume, García-Guerra Carlos E
Centre for Sensors, Instruments and Systems Development, Universitat Politècnica de Catalunya, Rambla de Sant Nebridi 10, Terrassa, 08222, Spain.
Biomed Opt Express. 2025 Jul 16;16(8):3194-3205. doi: 10.1364/BOE.562636. eCollection 2025 Aug 1.
Machine learning and deep learning have previously been used to predict the subjective refraction endpoint by objective means with modest success. This study aimed to enhance predictive accuracy by training linear regression models with normal equations using accommodative response and optical quality data. Three models were tested on 176 eyes, with input variables obtained from a Hartmann-Shack aberrometer and an autorefractor. The best model reduced mean absolute error by 40% compared to the objective refraction provided by a commercial autorefractometer and achieved 95% limits of agreement with subjective refraction of ±0.54 D, approaching the subjective refraction inter-examiner variability. Incorporating accommodative response data improved prediction accuracy over objective refraction alone and previous approaches.
机器学习和深度学习此前已被用于通过客观手段预测主观验光终点，但取得的成效一般。本研究旨在通过使用调节反应和光学质量数据训练采用正规方程的线性回归模型来提高预测准确性。在176只眼睛上测试了三种模型，输入变量来自哈特曼-夏克像差仪和自动验光仪。与商用自动验光仪提供的客观验光相比，最佳模型将平均绝对误差降低了40%，并在主观验光±0.54 D的范围内达到了95%的一致性界限，接近主观验光检查者间的变异性。纳入调节反应数据比单独的客观验光和先前的方法提高了预测准确性。
