**Purpose**:
Cataract surgery is the most common eye surgery. Appropriate optimization of intraocular lens (IOL) calculation formulae can result in improved patient outcomes. The purpose of this article is to describe a methodology of optimizing existing IOL formulae and develop hybrid formulae based on artificial intelligence (AI).

**Methods**:
Preoperative biometric and postoperative outcomes data were obtained from medical records at a single institution. A numeric computing environment was used to analyze these data and refine IOL formulae using supervised learning AI. The mean absolute error of each IOL formulae with and without AI enhancement was determined, as well as the number of eyes within 0.5 diopter of the predicted refraction.

**Results**:
AI algorithms improved the mean absolute error as well as number of eyes within 0.5 diopters of predicted refraction for each of the formulae tested (*P* < 0.05).

**Conclusions**:
A novel methodology is described that uses AI to improve existing IOL formulae. This methodology has the potential to improve clinical outcomes for cataract surgery patients.

**Translational Relevance**:
Artificial intelligence can be used to improve existing IOL formulae.

^{1}Second-generation formulae included factors that scaled the prediction based on axial length (AL).

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^{2}Third-generation formulae such as the Holladay 1, Hoffer Q, and SRK/T are theoretical mathematical formulae that are based on both vergence optics and a prediction of the effective lens position of the IOL.

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^{3}Further generations of formulae such as the Barrett Universal II and Haigis used the measured anterior chamber depth (ACD) as a predictor of the effective lens position.

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^{5}The original Ladas super formula (LSF) combined multiple formulae to enhance accuracy by using the most appropriate formula for a specific eye.

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^{7}There was no specific description of the algorithm and the main disadvantage as they pointed out was the requirement of “substantial computing power and memory.” More recently, other calculation methods that use some form of AI include the Hill-RBF,

^{8}the most recent version of the Kane formula,

^{9}the Sramka,

^{10}and the Pearl-DGS.

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^{12}Extreme gradient boosting (XGBoost) is one of the gradient boosting methods that assemble many weak prediction models used typically in decision trees.

^{13}XGBoost combines many decision trees to reveal the relationship between the input variables and output. An artificial neural network (ANN) is built up by nodes and layers. Each node is a nonlinear filter and each layer is built up by parallel nodes. The input will go through the network of those nodes and the weights of the connections will be learned by training the ANN model.

^{14}The combination of those nodes thus finally reveals the nonlinear relationship between the inputs and output to predict the outcome.

*P*value of less than 0.05 was considered statistically significant.

*P*< 0.5). Further, the percentage of eyes within 0.5 diopters of the predicted refraction improved with each generation (SRK = 62%, Holladay 1, =72% and LSF = 76%).

^{9}This outcome is similar to our results demonstrating a mean AE of 0.392 and 72% and 95% within 0.5 and 1.0 diopter of prediction with the standard Holladay I formula. The range of all mean AEs in the study by Darcy et al.

^{9}was 0.377 to 0.410 diopter for all of the theoretical formulae. The mean AE for the Kane formula was 0.377 and the Barrett Universal II was 0.390. The greatest number of eyes within 0.5 diopter of predicted refraction with any formula was 72%. The LSF was not included in that particular analysis.

^{15}However, this important variable does not occur in a vacuum and is likely intimately related to other variables such as the ACD. Further, it is doubtful that any AL adjustment should start and stop at exactly 25 mm. Others have proposed incorporating additional variables to account for a multitude of factors. In fact, the Holladay 2 formula released in 1992 includes additional variables of lens thickness, white to white distance, preoperative refraction, and age.

^{16}Other potential variables that have been suggested to have an effect on IOL prediction include the equatorial lens position, age, race, gender, aphakic refraction, relative ratio of various eye segments, C-factor, posterior corneal power, corneal thickness, specific lens design, and the exact power of the IOL.

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^{22}Again, these variables do not occur in a vacuum and may be interrelated. Deep learning can be used to weigh the effect of multiple variables on reaching a desired outcome. These advancements and adjustments are unlikely to be conceived as single variables or discrete formulae, and progress using such approaches likely would be inefficient compared to machine learning methods.

^{10}which used the clinical result and machine learning to modify the IOL power and predicted outcome. They were able to demonstrate an improvement in the prediction error.

**J. Ladas,**Advanced Euclidean Solutions, LLC (F);

**D. Ladas,**None;

**S.R. Lin,**None;

**U. Devgan,**Advanced Euclidean Solutions, LLC (F);

**A.A. Siddiqui,**Advanced Euclidean Solutions, LLC (F);

**A.S. Jun,**Advanced Euclidean Solutions, LLC (F)

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