Comparison of accuracy of different intraocular lens power calculation methods using artificial intelligence

Abstract

Purpose: To assess the accuracy of the intraocular lens (IOL) power calculation based on three methods using artificial intelligence (AI) and one formula using no AI. Methods: During cataract surgery on 114 eyes, one type of IOL was implanted, calculated with the Hill-RBF 2.0 method. The theoretical postoperative refractions were calculated using the Kane and the Pearl-DGS methods and a vergence based formula (Barrett Universal II, BUII). The differences between the manifest and objective postoperative refractions and the predicted refractions were calculated. The percentage of eyes within ±0.5 D and ±1.0 D prediction error (PE), the mean, and the median absolute errors (MAE and MedAE) were also determined. Results: The mean age of the patients was 69.48 years; the axial length was between 21.19 and 25.39 mm. The number of eyes within ±0.5/±1.0 D PE was 96/108 (84.21%/94.73%) using the Hill-RBF 2.0 method, 92/107 (80.70%/93.85%) with the Kane method, 91/107 (79.82%/93.85%) with the Pearl-DGS method, and 91/106 (79.82%/92.98%) with the BUII formula, using subjective refraction. With objective refractometric data, PEs were within ±0.5 D in 88 (77.19%), 83 (72.80%), 82 (71.92%), and 80 (70.17%) cases (Hill-RBF, Kane, Pearl-DGS, BUII, respectively). MAE and MedAE were also best with the Hill-RBF 2.0 method (0.3 D; 0.18 D). Conclusion: Better accuracy of PE might be obtained by the Hill-RBF 2.0 method compared with BUII. The Kane and Pearl-DGS methods showed similar accuracy when compared with BUII.