What question did this study set out to answer?

The study aims to evaluate the performance of the Zeiss AI IOL Calculator against Barrett Universal II and Kane in predicting postoperative refractive outcomes.

March 29, 2026Clinical ophthalmology0 citationsOpen Access

Refractive Outcomes of Cataract Surgery Using Zeiss Artificial Intelligence Calculator versus Barrett Universal II and Kane

NCNathan CannonPenn State Milton S. Hershey Medical Center LRLiam ReddenDean McGee Eye Institute KRKamran RiazDean McGee Eye Institute

Key Points

The study aims to evaluate the performance of the Zeiss AI IOL Calculator against Barrett Universal II and Kane in predicting postoperative refractive outcomes.
Conducted a multicenter retrospective case series.
Included surgery-naïve eyes undergoing cataract surgery.
Utilized preoperative optical biometry and monofocal IOL implantation.
Measured various prediction errors including MAE and RMSAE.
Statistical differences analyzed using Wilcox-Holladay-Wang-Koch tests.
ZAIC showed a lower mean absolute error (MAE) than BU2 TK (0.30 vs 0.32 D, p = 0.04).
ZAIC had lower standard deviation (SD) and root mean squared absolute error (RMSAE) compared to BU2 K and BU2 TK.
A greater proportion of eyes (82%) were within 0.5 D of predicted with ZAIC compared to 76% with BU2 K (p = 0.02).
ZAIC also performed better than BU2 TK for the proportion of eyes within 0.75 D (95% vs 91%, p = 0.02).
No significant differences in MedAE or proportions of eyes within 0.25 or 1 D were noted.

Abstract

Purpose: To compare the performance of the Zeiss AI IOL Calculator (ZAIC) to that of Barrett Universal II (BUII) and Kane, using standard (K) and total keratometry (TK) as inputs. Patients and Methods: This was a multicenter retrospective case series which included surgery-naïve eyes undergoing cataract surgery with preoperative optical biometry on the IOLMaster 700, monofocal IOL (Bausch 0.38 vs 0.41 (p = 0.008) and 0.42 (p = 0.008)]. ZAIC also had a larger proportion of eyes within 0.5 D of predicted compared to BU2 K 82% vs 76% (p = 0.02), as well as a larger proportion of eyes within 0.75 D of predicted compared to BU2 TK 95% vs 91% (p = 0.02). There were no significant differences in MedAE or the proportion of eyes within 0.25 or 1 D of predicted. Conclusion: Compared to BU2, ZAIC produces a lower MAE, SD, and RMSAE, and a higher proportion of eyes within 0.5 and 0.75 D of predicted but performs similarly to Kane across all outcome measures. This remains true regardless of whether K or TK is used as an input. This study is limited by its retrospective design and use of a single MX60E IOL model. Plain Language Summary: This multi-institutional retrospective case series compares the prediction accuracy and precision of the Zeiss AI IOL Calculator (ZAIC) to the Barrett Universal II (BU2) and Kane intraocular lens power prediction formulas for cataract surgery. Results suggest ZAIC outperforms BU2 and performs at least as well as Kane. This holds true regardless of whether standard or total keratometry is used as formula inputs. This corroborates ZAIC as an accurate and precise tool for the preoperative selection of intraocular lens power, performing at least as well as other modern intraocular lens power calculation formulas. Keywords: cataract extraction, intraocular lens, artificial intelligence

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