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. 2022 Oct;11(5):1869-1881.
doi: 10.1007/s40123-022-00551-6. Epub 2022 Aug 2.

Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length

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Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length

Chuang Li et al. Ophthalmol Ther. 2022 Oct.

Abstract

Introduction: To evaluate and compare the effectiveness for reducing the prediction error (PE) of the second eye using formula-specific factors, artificial intelligence (AI) formulas (PEARL-DGS and Kane), and the Cooke-modified axial length (CMAL) methods in bilateral cataract patients with long axial length (AL).

Methods: A total of 98 patients with long AL who underwent sequential bilateral cataract surgeries were retrospectively enrolled. The second-eye IOL power was calculated by the formula-specific factors, AI formulas, and CMAL methods when the first eye suffered from refraction surprise. The correction factors of eight formulas were calculated by regression analysis.

Results: There was a significant correlation between bilateral preoperative biometric parameters (P < 0.05) as well as bilateral PE (P < 0.05). The Kane formula displayed the lowest median absolute error (MedAE) and highest proportion of PE within ± 0.50 and ± 1.00 D compared with other formulas for the first eye. For the second-eye refinement, all three methods could reduce the second-eye MedAE. The formula-specific correction factors were 0.250, 0.331, 0.343, 0.394, 0.409, 0.452, 0.503, and 0.520 for Kane, Barrett Universal II (BUII), PEARL-DGS, Holladay 2, Holladay 1, Haigis, Hoffer Q, and SRK/T, respectively. The new AI-based Kane and PEARL-DGS with or without the CMAL methods could improve the refractive outcomes of the second eye in sequential bilateral cataract patients with long AL. The Kane, BUII, and PEARL-DGS with specific correction factors displayed higher accuracy compared with the other two methods (P < 0.05).

Conclusions: The new AI-based Kane and PEARL-DGS with or without the CMAL methods could improve the refractive outcomes of the second eye in sequential bilateral cataract patients with long AL. Notably, the Kane, PEARL-DGS, and BUII with specific correction factors displayed higher accuracy.

Keywords: Cooke-modified axial length; Formula-specific factors; IOL power calculation; Long axial length; Prediction error.

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Figures

Fig. 1
Fig. 1
Stacked histogram showing the percentages of the first eyes within ± 0.25, ± 0.50, ± 0.75, ± 1.00, and >  ± 1.00 D of the prediction error for the entire dataset. BUII Barrett Universal II; D diopter
Fig. 2
Fig. 2
Graphs showing the interocular correlation of the prediction error with different formulas for the entire dataset. A Kane formula (regression coefficient R = 0.250; P < 0.001); B BUII formula (regression coefficient R = 0.331; P < 0.001); C PEARL-DGS formula (regression coefficient R = 0.343; P < 0.001); D Holladay 2 formula (regression coefficient R = 0.394; P < 0.001); E Holladay 1 formula (regression coefficient R = 0.409; P < 0.001); F Haigis formula (regression coefficient R = 0.452; P < 0.001); G Hoffer Q formula (regression coefficient R = 0.503; P < 0.001); H SRK/T formula (regression coefficient R = 0.520; P < 0.001). Statistically significant (P < 0.05)
Fig. 3
Fig. 3
Stacked histogram showing the percentages of the second eyes of the prediction error within ± 0.25, ± 0.50, ± 0.75, ± 1.00, and >  ± 1.00 D when the first eyes suffered from refraction surprise. aSEPE-Kane adjusted second-eye prediction error with Kane, aSEPE BUII adjusted second-eye prediction error with BUII, aSEPE-DGS adjusted second eye prediction error with PEARL-DGS, Kane-CMAL Kane with CMAL adjustment, BUII-CMAL BUII with CMAL adjustment, DGS-CMAL PEARL-DGS with CMAL adjustment
Fig. 4
Fig. 4
Standard graphs showing the median absolute errors of the second eyes when the first eyes suffered from refraction surprise. aSEPE-Kane adjusted second-eye prediction error with Kane, aSEPE-BUII adjusted second-eye prediction error with BUII, aSEPE-DGS adjusted second-eye prediction error with PEARL-DGS, Kane-CMAL Kane with CMAL adjustment, BUII-CMAL BUII with CMAL adjustment, DGS-CMAL PEARL-DGS with CMAL adjustment

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