Accuracy of 7 Artificial Intelligence-Based Intraocular Lens Power Calculation Formulas in Extremely Long Caucasian Eyes.

Abstract

To compare 7 artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in extremely long eyes. Retrospective accuracy and validity analysis. Kyiv Clinical Ophthalmology Hospital Eye Microsurgery Center, Ukraine. Patients with highly myopic eyes, who underwent uneventful phacoemulsification. IOL power was calculated before cataract surgery. The power of the implanted IOL was randomly selected from the outcomes of SRK/T, Holladay 2, or Barrett Universal II. Three months after phacoemulsification, refraction was measured. Postsurgery IOL power calculations were performed using the following formulas: Hill-RBF 3.0, Kane, PEARL-DGS, Ladas Super Formula AI (LSF AI), Hoffer QST, Karmona, and Zhu-Lu. Root mean square absolute error (RMSAE), median absolute error (MedAE), and percentage of eyes with prediction error within ±0.50 D. Forty-eight eyes with axial length >30.00 mm were studied. Hill-RBF 3.0 yielded the lowest RMSAE (0.788) with statistical superiority only over Karmona (0.956, P = .021). In terms of MedAE, outcomes obtained by Hoffer QST (0.442) and Hill-RBF (0.490) were statistically significant compared with LSF AI (0.800, P = .013 and P = .008, respectively). The highest percentage of eyes with prediction error within ±0.50 D was achieved by Hill-RBF 3.0, Kane, and Hoffer QST (54.17% each) statistically significant as follows: both Hill-RBF and Kane compared with LSF AI (27.08%) and Karmona (39.58%), and Hoffer QST compared with LSF AI. All tested formulas demonstrated comparable trueness, with Hill-RBF 3.0 being more accurate than Karmona (RMSAE), and LSF AI being less accurate than Hoffer QST and Hill-RBF 3.0 (MedAE).