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

Purpose: To compare 7 artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in extremely long eyes.

Design: Retrospective accuracy and validity analysis.

Setting: Kyiv Clinical Ophthalmology Hospital Eye Microsurgery Center, Ukraine.

Study Population: Patients with highly myopic eyes, who underwent uneventful phacoemulsification.

Observation Procedures: 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.

Main Outcome Measures: Root mean square absolute error (RMSAE), median absolute error (MedAE), and percentage of eyes with prediction error within ±0.50 D.

Results: 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.

Conclusion: 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).