Journal of cataract and refractive surgery · 2024
Evaluation of an artificial intelligence-based intraocular lens calculator: AI-based IOL-optimized formula.
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Wang Li, Burwinkel Hendrik, Bensaid Nicolas, Koch Douglas D
Аннотация
PURPOSE: To evaluate the ZEISS AI IOL Calculator (ZEISS AI) and compare its accuracy in refractive prediction to the Barrett Universal II (BUII) and Kane formulas.
SETTING: Cullen Eye Institute, Baylor College of Medicine, Houston, TX.
DESIGN: Retrospective case series.
METHODS: The ZEISS AI IOL Calculator (ZEISS AI) is an artificial intelligence (AI) based IOL-optimized formula. The refractive prediction errors (PEs) were calculated in the entire dataset and subgroups of short eyes (axial length (AL) ≤ 22.5 mm) and long eyes (AL ≥ 25.0 mm). The standard deviation (SD), root-mean-square absolute error (RMSAE), mean absolute error (MAE), median absolute error (MedAE), and percentage of eyes within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D of PEs were calculated. Values with ZEISS AI were compared to those from Barrett Universal II (BUII) and Kane. Advanced statistical methods were applied using R.
RESULTS: A dataset of 10,838 eyes was included. Compared to ZEISS AI, BUII produced significantly greater SDs, RMSAEs, and MAEs in the whole group and short eyes, and the Kane had greater SD, RMSAE, and MAE in short eyes (all adjusted P<0.05); the BUII had significantly lower percentages of eyes within ±0.50 D of PEs in the whole group (80.0% vs 81.2%) and in short eyes (71.3% vs. 76.1%), and the Kane had lower percentage of eyes within ±0.50 D of PEs in short eyes (71.9% vs. 76.1%) (all adjusted P<0.05).
CONCLUSION: The ZEISS AI IOL Calculator had superior performance compared to the BUII and Kane formulas, especially in short eyes.
SETTING: Cullen Eye Institute, Baylor College of Medicine, Houston, TX.
DESIGN: Retrospective case series.
METHODS: The ZEISS AI IOL Calculator (ZEISS AI) is an artificial intelligence (AI) based IOL-optimized formula. The refractive prediction errors (PEs) were calculated in the entire dataset and subgroups of short eyes (axial length (AL) ≤ 22.5 mm) and long eyes (AL ≥ 25.0 mm). The standard deviation (SD), root-mean-square absolute error (RMSAE), mean absolute error (MAE), median absolute error (MedAE), and percentage of eyes within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D of PEs were calculated. Values with ZEISS AI were compared to those from Barrett Universal II (BUII) and Kane. Advanced statistical methods were applied using R.
RESULTS: A dataset of 10,838 eyes was included. Compared to ZEISS AI, BUII produced significantly greater SDs, RMSAEs, and MAEs in the whole group and short eyes, and the Kane had greater SD, RMSAE, and MAE in short eyes (all adjusted P<0.05); the BUII had significantly lower percentages of eyes within ±0.50 D of PEs in the whole group (80.0% vs 81.2%) and in short eyes (71.3% vs. 76.1%), and the Kane had lower percentage of eyes within ±0.50 D of PEs in short eyes (71.9% vs. 76.1%) (all adjusted P<0.05).
CONCLUSION: The ZEISS AI IOL Calculator had superior performance compared to the BUII and Kane formulas, especially in short eyes.