**Purpose**:
To develop a method for predicting postoperative anterior chamber depth (ACD) in cataract surgery patients based on preoperative biometry, demographics, and intraocular lens (IOL) power.

**Methods**:
Patients who underwent cataract surgery and had both preoperative and postoperative biometry measurements were included. Patient demographics and IOL power were collected from the Sight Outcomes Research Collaborative (SOURCE) database. A gradient-boosting decision tree model was developed to predict the postoperative ACD. The mean absolute error (MAE) and median absolute error (MedAE) were used as evaluation metrics. The performance of the proposed method was compared with five existing formulas.

**Results**:
In total, 847 patients were assigned randomly in a 4:1 ratio to a training/validation set (678 patients) and a testing set (169 patients). Using preoperative biometry and patient sex as predictors, the presented method achieved an MAE of 0.106 ± 0.098 (SD) on the testing set, and a MedAE of 0.082. MAE was significantly lower than that of the five existing methods (*P* < 0.01). When keratometry was excluded, our method attained an MAE of 0.123 ± 0.109, and a MedAE of 0.093. When IOL power was used as an additional predictor, our method achieved an MAE of 0.105 ± 0.091 and a MedAE of 0.080.

**Conclusions**:
The presented machine learning method achieved greater accuracy than previously reported methods for the prediction of postoperative ACD.

**Translational Relevance**:
Increasing accuracy of postoperative ACD prediction with the presented algorithm has the potential to improve refractive outcomes in cataract surgery.

^{1}introduced two additional variables, preoperative anterior chamber depth (ACD) and preoperative crystalline lens thickness as predictors for postoperative IOL position.

^{2}estimated in 2008 that estimates of IOL position were responsible for 36% of the error in IOL power predictions.

^{3}pointed out in 2003, exact postoperative ACD prediction based on preoperative biometry data is, in principle, impossible because of the effect of several uncertain parameters including the shrinkage of the capsular bag. There have, however, been reports of various preoperative features that may be predictive of postoperative ACD. For example, Plat et al.

^{4}in 2017 reported correlation between measurements of axial length (AL), horizontal white-to-white distance (WTW), and preoperative ACD with postoperative ACD. Other approaches have added corneal power to improve postoperative ACD prediction.

^{5}

^{6}

^{,}

^{7}However, these approaches rely on angle-to-angle measurements that are not typically obtained in a standard cataract surgery preoperative workup. Furthermore, these measurements involve manual caliper-based measurements, introducing subjectivity and variability into the measurements while slowing the workflow of the cataract surgeon.

*x*preoperative records and

*y*postoperative records had

*xy*possible combinations. The inclusion of all possible preoperative and postoperative biometry record combinations represents a form of data augmentation, with the intention of increasing robustness to measurement variations while recognizing that the same eye can have varying lens thickness and preoperative anterior chamber depth due to natural cataract progression. At the end of data preprocessing (Fig. 1, middle panel), a dataset of 4137 samples that involved 847 distinct patients was generated and used for the development of the machine learning model. Each sample consisted of (1) preoperative biometry: AL, central corneal thickness (CCT), ACD, crystalline lens thickness (LT), flat keratometry K1, steep keratometry K2, \(Km = \frac{{K1 + K2}}{2}\), and horizontal white-to-white (WTW), (2) patient sex, (3) IOL power, and (4) postoperative ACD, where (1) to (3) were the predictors and (4) was the target variable in the machine learning model.

*IOL*) was replaced by

_{old}*n*randomly selected IOL powers, and the ground truth postoperative ACD was adjusted based on the selected IOL powers (see Supplementary Fig. S1). Specifically, for each distinct patient,

_{IOL}*n*synthetic IOL powers (

_{IOL}*IOL*

_{new,1},

*IOL*

_{new,2}, …) between [

*IOL*,

_{min}*IOL*] were selected, and the adjusted (new) postoperative ACD corresponding to each new IOL power was calculated as

_{max}*m*∈ [0, 1] is a constant,

*IOL*≥ 6,

_{min}*IOL*≤ 30. The value of

_{max}*IOL*,

_{min}*IOL*,

_{max}*m*, and

*n*were optimized through cross-validation. In data interpolation,

_{IOL}*k*samples were randomly picked, and the center of those

*k*samples was calculated by averaging each dimension of the predictor vector

*X*and the target value

*y*. Categorical variables were treated as continuous variables. The number of samples,

*k*, used to create each synthetic sample and the number of samples generated

*, n*, were optimized through cross-validation.

*IOL*,

_{min}*IOL*,

_{max}*m*,

*n*,

_{IOL}*n*, and

*k*) and the hyperparameters in the machine learning model (the learning rate, number of estimators, maximum tree depth, and number of leaves). Cross-validation was also used to evaluate the performance of different subsets of features. Mean absolute error (MAE) in postoperative ACD prediction was used as the primary evaluation metric in cross-validation. The optimal models for three scenarios: (1) Base (2) Base + IOL (3) Base − K were selected on the basis of the mean of the MAEs in the cross-validation results.

^{1}

^{,}

^{8}

^{–}

^{13}The lens constants were optimized for each formula to eliminate systematic errors in refraction prediction using previously described methods.

^{1}

^{,}

^{14}

^{,}

^{15}The optimized constants were: 1.655 for Haigis, 5.844 for Hoffer Q, 1.990 for Holladay I, −0.225 for Olsen, and 119.303 for SRK/T. The corresponding mean errors in refraction are listed in Supplementary Table S1. We further compared our methods to two baseline prediction methods: (1) average postoperative ACD, which used the average postoperative ACD in the training/validation dataset as the predicted ACD for the testing set and (2) linear regression, which used AL, CCT, ACD, LT, K1, K2, Km, and WTW as predictors. Data augmentation (i.e., interpolation and IOL augmentation) was not applied to the linear regression model.

*r*) were also calculated for the performance comparison in the testing set. To gain insights into the relative importance of predictors in the machine learning model, we calculated the total gain (total reduction in training loss) across splits in decision trees for each predictor in the model.

^{2}test was performed to evaluate the difference in the proportion of males and females among all patients. A two-tailed Student

*t*-test was performed to evaluate for differences in the means of biometry values between males and females. The Pearson correlation coefficients and the

*P*values testing the significance of correlation were calculated between the postoperative ACD and the preoperative biometry measurements. To assess the difference in cross-validation results of different methods, a Wilcoxon sign-rank test was performed. The testing set results of different methods were compared on the basis of the Friedman test followed by a post hoc paired Wilcoxon signed-rank test with a Bonferroni correction. Performance of the keratometry-independent Base − K model was compared between the testing sets of patients with and without prior refractive surgery through an unpaired two-sample Wilcoxon signed-rank test (i.e., the Mann-Whitney U test). Statistical significance for all above tests was defined as

*P*value < 0.05. All statistical analysis and machine learning model construction scripts were written in Python 3.

^{2}test,

*P*< 0.01). The postoperative ACD was positively correlated with preoperative AL, ACD, WTW, and CCT (

*P*< 0.01 for each) and negatively correlated with preoperative LT and WTW (

*P*< 0.01 for each). Postoperative ACD was not significantly correlated with preoperative Km (

*P*= 0.74) (Fig. 2A). Figure 2B shows the distribution of the power of the implanted IOL and the postoperative lens thickness (

*r*= 0.75,

*P*< 0.01). The scatter plot indicates a linear relationship between the IOL power and postoperative IOL thickness. The distributions of biometry measurements in male and female patients are shown in Figure 2C. The preoperative AL, preoperative ACD, and postoperative ACD in male patients were longer than those in female patients (

*P*< 0.01 for each). Km in females was greater than that in males (

*P*< 0.01).

*P*< 0.01), as expected. For comparison purposes, we recalculated the cross-validation results using median absolute error as the evaluation metric. The results were as follows: 0.100 mm for Base, 0.097 mm for Base + IOL, and 0.108 mm for Base − K. The prediction performance was consistent with the results obtained with MAE.

*P*< 0.01). The Base predictors, which included preoperative biometry and patient sex, achieved an MAE of 0.106 mm. Adding the IOL improved the prediction performance in the test set (MAE = 0.105 mm). Base and Base + IOL significantly outperformed Haigis, Hoffer Q, Holladay I, Olsen, SRK/T, and mean postoperative ACD, based on the post hoc Wilcoxon signed rank test with Bonferroni correction (

*P*< 0.01). When the corneal power was not included (Base − K), which simulates the scenario when the measured corneal power is not reliable, our method maintained good performance, with an MAE = 0.123 mm. The performance of Base − K still significantly outperformed the existing five formulas (

*P*< 0.01). When we tested the Base − K model on patients with prior refractive surgery (Table 3), the MAE was 0.129 mm, and this result was not significantly different compared with the performance of Base − K for patients with no history of refractive surgery (

*P*= 0.13).

*r*). The performance of linear regression on our biometry dataset also exceeded that of previously reported AS-OCT methods by

*R*

^{2}value.

^{6}Because lens constants were optimized individually before the predictions of each of the aforementioned formulas were computed, the high performance of linear regression relative to existing methods was likely due to the size of the dataset of available, as well as the use of optical biometry to directly measure postoperative IOL position, as opposed to ultrasound biometry or ELP calculations. The existing formulas considered here use a thin lens assumption, wherein the intermediate value referred to as the ACD does not represent the position of either surface of the IOL, but rather the location of the principal plane.

^{5}Therefore the estimated ACD terms in these formulas can more accurately be described as providing information about the ELP within the optical models employed by those IOL power calculation formulas. As such, they are not ideal for prediction of the true postoperative anatomy of the eye of a cataract surgery patient.

^{7}

^{,}

^{16}Evaluation of feature importance demonstrated that preoperative ACD was the most important input parameter, followed by crystalline LT, AL, and horizontal WTW, respectively. Inclusion of patient sex, which is not typically used in methods of postoperative ACD prediction, in the model was found to improve performance (Supplementary Figure S2). This finding was consistent with prior studies of patient biometry reporting consistent differences in ocular shape between male and female patients, with female corneal powers measuring greater and axial lengths measuring shorter than those of males on average.

^{17}

^{,}

^{18}

^{19}and may be applicable in new methods for IOL power calculation in patients with prior refractive surgery. Performance of this K-independent model was not significantly different for patients with or without prior refractive surgery, indicating its applicability to the postrefractive surgery population.

**T. Li,**None;

**K. Yang,**None;

**J.D. Stein,**None;

**N. Nallasamy,**None

*J Cataract Refract Surg*. 1995; 21(3): 313–319. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 2008; 34(3): 368–376. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 2003; 29(11): 2122–2126. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 2017; 43(2): 195–200. [CrossRef] [PubMed]

*Br J Ophthalmol*. 2017; 101(10): 1440–1446. [CrossRef] [PubMed]

*Ophthalmology*. 2016; 123(12): 2474–2480. [CrossRef] [PubMed]

*Transl Vis Sci Technol*. 2019; 8(3).

*J Cataract Refract Surg*. 1990; 16(3): 333–340. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 1993; 19(6): 700–712. [CrossRef] [PubMed]

*Graefe's Arch Clin Exp Ophthalmol*. 2000; 238(9): 765–773. [CrossRef]

*J Cataract Refract Surg*. 1988; 14(1): 17–24. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 1990; 16(4): 528. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 1994; 20(6): 677. [CrossRef]

*J Cataract Refract Surg*. 2016; 42(10): 1490–1500. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 2017; 43(8): 999–1002. [CrossRef] [PubMed]

*Ophthalmology*. 2016; 123(12): 2474–2480. [CrossRef] [PubMed]

*Acta Ophthalmol*. 2014; 92(8): 759–763. [CrossRef] [PubMed]

*Int Ophthalmol Clin*. 2017; 57(3): 137–142. [CrossRef] [PubMed]

*J Cataract Refract Surg*. 2011; 37(3): 506–512. [CrossRef] [PubMed]