A Comparison of Intraocular Lens Power Calculation Formulas in High Myopia

Comparing the accuracy of intraocular lens power calculation formulas using artificial intelligence and traditional formulas in highly myopic patients: a meta-analysis

PURPOSE

The accuracy of intraocular lens (IOL) calculations is one of the key indicators for determining the success of cataract surgery. However, in highly myopic patients, the calculation errors are relatively larger than those in general patients. With the continuous development of artificial intelligence (AI) technology, there has also been a constant emergence of AI-related calculation formulas. The purpose of this investigation was to evaluate the accuracy of AI calculation formulas in calculating the power of IOL for highly myopic patients.

METHODS

We searched the relevant literature through August 2023 using three databases: PubMed, EMBASE, and the Cochrane Library. Six IOL calculation formulas were compared: Kane, Hill-RBF, EVO, Barrett II, Haigis, and SRK/T. The included metrics were the mean absolute error (MAE) and percentage of errors within ± 0.25 D, ± 0.50 D, and ± 1.00 D.

RESULTS

The results showed that the MAE of Kane was significantly lower than that of Barrett II (mean difference = - 0.03 D, P = 0.02), SRK/T (MD = - 0.08 D, P = 0.02), and Haigis (MD = - 0.12 D, P < 0.00001). The percentage refractive prediction errors for Kane at ± 0.25 D, ± 0.50 D, and ± 1.00 D were significantly greater than those for SRK/T (P = 0.007, 0.003, and 0.01, respectively) and Haigis (P = 0.009, 0.0001, and 0.001, respectively). No statistically significant differences were noted between Hill-RBF and Barret, but Hill-RBF was significantly better than SRK/T and Haigis.

CONCLUSION

The AI calculation formulas showed more accurate results compared with traditional formulas. Among them, Kane has the best performance in calculating IOL degrees for highly myopic patients.

Accuracy of 14 intraocular lens power calculation formulas in extremely long eyes

PURPOSE

To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0 mm.

METHODS

In this retrospective study, 211 eyes (211 patients) with ALs > 30.0 mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed.

RESULTS

The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran's Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%).

CONCLUSIONS

The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0 mm.

Accuracy of the Majority Voting Method with Multiple IOL Power Formulae

Purpose

This study aimed to evaluate the efficacy of a majority decision algorithm that integrates intraoperative aberrometry (IA) and two intraocular lens (IOL) frequency formulas. The primary objective was to compare the accuracy of three formulas (IA; Sanders, Retzlaff, and Kraff/Theoretical (SRK/T); and Barrett Universal II (BUII)), in achieving emmetropia in eyes implanted with TFNT lenses (Alcon).

Patients and Methods

A total of 145 eyes of 145 patients were included in the evaluation. Preoperative data were obtained from IOLMaster 700, while intraoperative data were collected from ORA SYSTEMTM. Visual acuity ≥0.8 at the 3-month post-surgery mark was confirmed. We assessed refractive prediction error (RPE), which is the difference between predicted refraction (PR) and postoperative subjective refraction. This evaluation aimed to identify the optimal IOL power with the implemented algorithm.

Results

Among the 145 eyes evaluated, 55.9%, 78.7%, and 97.2% achieved postoperative subjective refraction within ±0.13 Diopters (D), ±0.25 D, and ±0.50 D, respectively. The percentages of eyes within ±0.25 D of PR varied by formula type, with values of 57%, 57%, and 54% for IA, BUII, and SRK/T, respectively. For eyes with short to medium axial length (AL<26.00 mm), the percentages within ±0.25 D of RPE were 52%, 58%, and 58% for IA, SRK/T, and BUII, respectively. In contrast, for eyes with long axial length (≥26.00 mm) the percentages were 68%, 52%, and 45% for IA, BUII, and SRK/T, respectively.

Conclusion

The proposed majority decision algorithm incorporating IA and two IOL frequency formulas was effective in reducing postoperative refractive error. IA was particularly beneficial for eyes with long axial length. These findings suggest the algorithm has potential to optimize IOL power selection to improve quality of life of patients and clinical practice outcomes.

Biometry challenges in the longest eyes we have encountered to date

Purpose

This report aims to present biometry challenges and solutions for a patient with the longest eyes we have encountered to date.

Observations

A 41-year-old woman with a history of Crouzon syndrome, extreme axial myopia, and posterior segment staphylomas was referred for cataract evaluation. Optical biometry was attempted using two partial coherence interferometry and optical low-coherence reflectometry devices that were available in 2011. Neither device could measure the axial length (AL) of either eye, unfortunately. We were able to measure them by A scan ultrasound, however, with results of 40.59 mm for the right eye and 38.29 mm for the left eye. Shortly thereafter, she underwent uncomplicated phacoemulsification with posterior chamber intraocular lens implantation under topical anesthesia. Twelve years later, she returned for repeat optical biometry with 3 newer generation devices, 2 of which utilized swept-source optical coherence tomography (SS-OCT). Only 1 SS-OCT device, the Argos biometer, was able to obtain AL measurements, and they were 40.54 mm and 40.84 mm for the right and left eyes, respectively.

Conclusions and importance

Biometry measurement using optical biometers on a patient with ALs greater than 40 mm was impossible in 2011 because of the relatively short gate for acceptable readings. Ultrasound biometry can also be challenging due to the presence of posterior staphylomas. However, a newer SS-OCT with a longer AL measurement capability enabled readings to be obtained more recently.

How to choose the intraocular lens power calculation formulas in eyes with extremely long axial length? A systematic review and meta-analysis

OBJECTIVE

To evaluate the accuracy of 10 formulas for calculating intraocular lens (IOL) power in cataract eye with an axial length (AL) of more than 28.0 mm.

METHODS

We searched scientific databases including PubMed, EMBASE, Web of Science and Cochrane Library for research published over the past 5 years, up to Sept 2023. The inclusion criteria were case series studies that compared different formulas (Barrett II, EVO, Kane, Hill-RBF, Haigis, Hoffer Q, Holladay 1, SRK/T, Holladay 1 w-k and SRK/T w-k), in patients with extremely long AL undergoing uncomplicated cataract surgery with IOL implantation. The mean difference (MD) of mean absolute error (MAE) and the odds ratio (OR) of both the percentage of eyes within ±0.50D of prediction error (PPE±0.50D) and the percentage of eyes within ±1.00D of prediction error (PPE±1.00D) among different formulas were pooled using meta-analysis.

RESULTS

A total of 11 studies, involving 1376 eyes, were included to evaluate the 10 formulas mentioned above. Among these formulas, Barrett II, EVO, Kane, and Hill-RBF demonstrated significantly lower MAE values compared to SRK/T. Furthermore, Kane and Hill-RBF had lower MAE values than EVO. Additionally, Barrett II and Kane yielded significantly lower MAE values than Haigis while Hill-RBF showed significantly lower MAE values than Holladay 1. Moreover, Hill-RBF showed the highest values for both PPE±0.50D and PPE±1.00D, followed by Kane. Both EVO and Kane had higher values of PPE±0.50D and PPE±1.00D compared to Haigis and SRK/T.

CONCLUSION

The Wang-Koch adjusted formulas and new-generation formulas have shown potential for higher accuracy in predicting IOL power for cataract patients with extremely long AL compared to traditional formulas. Based on the current limited clinical studies, Hill-RBF and Kane formulas seem to be a better choice for eyes with extremely long AL.

Network Meta-analysis of Intraocular Lens Power Calculation Formula Accuracy in 1016 Eyes With Long Axial Length

PURPOSE

To systematically review the literature and quantitatively synthesize the currently available evidence to compare the accuracy of different intraocular lens calculation formulas in eyes with long axial length (AL).

DESIGN

Network meta-analysis.

METHODS

PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for studies published between January 2000 and June 2022. Included were prospective or retrospective clinical studies reporting the following outcomes in cataract patients with long AL (ie, ≥26 mm): percentage of eyes with a prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D). Network meta-analysis was conducted using R software (version 4.2.1).

RESULTS

Ten prospective or retrospective clinical studies, including 1016 eyes and 11 calculation formulas, were identified. A traditional meta-analysis showed that for the percentage of eyes with PE within ±0.25 and ±0.50 D, the Olsen, Kane, and Emmetropia Verifying Optical (EVO) all had insignificantly higher percentages compared with others. Considering the percentage of eyes with PE within ±1.00 D, the original and modified Wang-Koch adjustment formulas for Holladay 1 (H1-WK and H1-MWK) and EVO formulas showed superiority, but the difference was insignificant. This network meta-analysis revealed that compared with the widely used Barrett Universal II (BUII) formula, the Olsen, Kane, and EVO formulas had higher percentages of eyes with PE within ±0.25, ±0.50, and ±1.00 D (all odds ratios >1 but P >.05). Based on the surface under the cumulative ranking area (SUCRA) values for the percentage of eyes with PE within ±0.25 D, the Olsen (96.4%), Kane (77.5%), and EVO (75.9%) formulas had the highest probability of being in the top 3 of the 11 formulas.

CONCLUSIONS

The Olsen, Kane, and EVO formulas may perform better than others in calculating IOL power in eyes with long AL. Nevertheless, there is still considerable uncertainty in this regard and the accuracy of these formulas in highly myopic eyes should be confirmed in studies based on large multicenter registries.

Influence of Ocular Biometry Parameters on the Predictive Accuracy of IOL Power Formulas in Patients with High Myopia

INTRODUCTION

The aim of this study was to investigate the influence of ocular biometry parameters on the predictive accuracy of 10 intraocular lens (IOL) power formulas in patients with high myopia (HM).

METHODS

We analyzed 202 eyes of 202 patients. The ocular biometry was determined preoperatively using an IOLMaster 700. The associations between the biometry parameters and the prediction error (PE) 1 month postoperatively were assessed. HM was defined as an axial length exceeding 26.50 mm.

RESULTS

In patients with HM (n = 108), the K6, Emmetropia Verifying Optical (EVO), Olsen, and Barrett Universal II (BUII) formulas had the lowest absolute PEs among the 10 formulas. The ocular biometry parameters were not associated with the PE of K6, EVO, Olsen, or BUII. A longer axial length in HM eyes was associated with myopic outcomes by Kane, Hoffer QST, and VRF and hyperopic outcomes by Holladay 2 and T2. Steeper keratometry, a deeper anterior chamber, and a thicker lens were associated with a hyperopic shift in HM eyes when using VRF, Kane, and Hoffer QST, respectively. In patients without HM (n = 94), there was no difference between the formulas in absolute PE. The significant associations between the biometry parameters and PE in patients with HM were not present in patients without HM.

CONCLUSIONS

K6, EVO, Olsen, and BUII displayed high accuracy in HM eyes and were not influenced by preoperative biometry parameters. For the remaining formulas, the preoperative keratometry, anterior chamber depth, lens thickness, and axial length were possible error sources underlying an inaccurate IOL power prediction in patients with HM.

Efficacy of corneal curvature on the accuracy of 8 intraocular lens power calculation formulas in 302 highly myopic eyes

PURPOSE

To investigate the effect of corneal curvature (K) on the accuracy of 8 intraocular lens formulas in highly myopic eyes.

SETTING

Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China.

DESIGN

Retrospective consecutive case series.

METHODS

302 eyes (302 patients) were analyzed in subgroups based on the K value. The mean refractive error, mean absolute error (MAE), median absolute error (MedAE), root-mean-square absolute prediction error (RMSAE) and proportions of eyes within ±0.25 diopter (D), ±0.50 D, ±0.75 D, ±1.00 D were statistical analyzed.

RESULTS

Emmetropia Verifying Optical (EVO) 2.0, Kane, and Radial Basis Function (RBF) 3.0 had the lower MAE (≤0.28) and RMSAE (≤0.348) and highest percentage of eyes within ±0.50 D (≥83.58%) in the flat (K ≤ 43 D) and steep K (K > 45 D) groups. Hoffer QST had the lowest MedAE (0.19), RMSAE (0.351) and the highest percentage of eyes within ±0.50 D (82.98%) in the normal K group (43 < K ≤ 45 D). When axial length (AL) ≤28 mm, all formulas showed close RMSAE values (0.322 to 0.373) in flat K group. When AL >28 mm, RBF 3.0 achieved the lowest MAE (≤0.24), MedAE (≤0.17) and RMSAE (≤0.337) across all subgroups.

CONCLUSIONS

EVO 2.0, Kane, and RBF 3.0 were the most accurate in highly myopic eyes with a flat or steep K. Hoffer QST is recommended for long eyes with normal K values. RBF 3.0 showed the highest accuracy when AL >28 mm, independent of corneal curvature.

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis

Objective

To systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.

Methods

We screened PubMed, Web of Science, Embase, and Cochrane Library databases for studies published from inception to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0.

Results

Twelve studies involving 2,430 adult myopic eyes (with axial lengths >26.0 mm) that underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas, as per the surface under the cumulative ranking curve (SUCRA) values, were XGBoost, Hill-RBF, and Kane. The three formulas had the lowest MedAE and were more accurate than traditional vergence formulas, such as SRK/T, Holladay 1, Holladay 2, Haigis, and Hoffer Q regarding MAE, percentage of eyes with PE within ±0.25, ±0.50, and ±1.00 D.

Conclusions

The top AI-based formulas for calculating IOL power in highly myopic eyes were XGBoost, Hill-RBF, and Kane. They were significantly more accurate than traditional vergence formulas and ranked better than formulas with Wang-Koch AL modifications or newer generations of formulas such as Barrett and Olsen.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335969.

Influence of Pilocarpine Eyedrops on the Ocular Biometric Parameters and Intraocular Lens Power Calculation

Objective

To evaluate the influence of pilocarpine eyedrops on the ocular biometric parameters and whether these parameter changes affect the intraocular lens (IOL) power calculation in patients with primary angle-closure glaucoma (PACG).

Methods

Twenty-two PACG patients and fifteen normal subjects were enrolled. Ocular biometric parameters including the axial length (AL), anterior chamber depth (ACD), lens thickness (LT), mean keratometry (Km), and white-to-white distance (WTW) were measured by using a Lenstar LS 900 device before and at least 30 minutes after instillation of 2% pilocarpine eyedrops. Lens position (LP) was calculated, and the IOL power prediction based on the ocular biometric parameters was performed using the Barrett Universal II, Haigis, Hoffer Q, Holladay I, or SRK/T formulas before and after pilocarpine application.

Results

In both PACG and normal groups, pilocarpine eyedrops induced a slight but statistically significant increase in the mean AL (0.01 mm for both groups) and mean LT (0.02 mm and 0.03 mm, respectively) but a significant decrease in the mean ACD (0.03 mm and 0.05 mm, respectively) and mean LP (0.02 mm and 0.04 mm, respectively). No significant changes in the mean Km and WTW were noticed in both groups. In addition, the IOL power calculation revealed insignificant changes before and after the pilocarpine instillation in both groups, regardless of the formula used.

Conclusions

Pilocarpine eyedrops can induce slight changes in the ocular biometric parameters including the AL, ACD, LT, and LP. However, these parameter changes will not result in a significant difference in IOL power estimation.

The Zhu-Lu formula: a machine learning-based intraocular lens power calculation formula for highly myopic eyes

BACKGROUND

To develop a novel machine learning-based intraocular lens (IOL) power calculation formula for highly myopic eyes.

METHODS

A total of 1828 eyes (from 1828 highly myopic patients) undergoing cataract surgery in our hospital were used as the internal dataset, and 151 eyes from 151 highly myopic patients from two other hospitals were used as external test dataset. The Zhu-Lu formula was developed based on the eXtreme Gradient Boosting and the support vector regression algorithms. Its accuracy was compared in the internal and external test datasets with the Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Kane, Pearl-DGS and Radial Basis Function (RBF) 3.0 formulas.

RESULTS

In the internal test dataset, the Zhu-Lu, RBF 3.0 and BUII ranked top three from low to high taking into account standard deviations (SDs) of prediction errors (PEs). The Zhu-Lu and RBF 3.0 showed significantly lower median absolute errors (MedAEs) than the other formulas (all P < 0.05). In the external test dataset, the Zhu-Lu, Kane and EVO 2.0 ranked top three from low to high considering SDs of PEs. The Zhu-Lu formula showed a comparable MedAE with BUII and EVO 2.0 but significantly lower than Kane, Pearl-DGS and RBF 3.0 (all P < 0.05). The Zhu-Lu formula ranked first regarding the percentages of eyes within ± 0.50 D of the PE in both test datasets (internal: 80.61%; external: 72.85%). In the axial length subgroup analysis, the PE of the Zhu-Lu stayed stably close to zero in all subgroups.

CONCLUSIONS

The novel IOL power calculation formula for highly myopic eyes demonstrated improved and stable predictive accuracy compared with other artificial intelligence-based formulas.

Accuracy of Six Intraocular Lens Power Calculations in Eyes with Axial Lengths Greater than 28.0 mm

The purpose of this study was to compare the accuracy of several intraocular (IOL) lens power calculation formulas in long eyes. This was a single-site retrospective consecutive case series that reviewed patients with axial lengths (AL) > 28.0 mm who underwent phacoemulsification. The Wang−Koch (WK) adjustment and Cooke-modified axial length (CMAL) adjustment were applied to Holladay 1 and SRK/T. The median absolute error (MedAE) and the percentage of eyes with prediction errors ±0.25 diopters (D), ±0.50 D, ±0.75 D, and ±1.00 D were used to analyze the formula’s accuracy. This study comprised a total of 35 eyes from 25 patients. The Kane formula had the lowest MedAE of all the formulas, but all were comparable except Holladay 1, which had a significantly lower prediction accuracy with either AL adjustment. The SRK/T formula with the CMAL adjustment had the highest accuracy in predicting the formula outcome within ±0.50 D. The newer formulas (BU-II, EVO, Hill-RBF version 3.0, and Kane) were all equally predictable in long eyes. The SRK/T formula with the CMAL adjustment was comparable to these newer formulas with better outcomes than the WK adjustment. The Holladay 1 with either AL adjustment had the lowest predictive accuracy.

Investigating the Prediction Accuracy of Recently Updated Intraocular Lens Power Formulas with Artificial Intelligence for High Myopia

The aim of this study was to investigate the prediction accuracy of intraocular lens (IOL) power formulas with artificial intelligence (AI) for high myopia. Cases of highly myopic patients (axial length [AL], >26.0 mm) undergoing uncomplicated cataract surgery with at least 1-month follow-up were included. Prediction errors, absolute errors, and percentages of eyes with prediction errors within ±0.25, ±0.50, and ±1.00 diopters (D) were compared using five formulas: Hill-RBF3.0, Kane, Barrett Universal II (BUII), Haigis, and SRK/T. Seventy eyes (mean patient age at surgery, 64.0 ± 9.0 years; mean AL, 27.8 ± 1.3 mm) were included. The prediction errors with the Hill-RBF3.0 and Kane formulas were statistically different from the BUII, Haigis, and SRK/T formulas, whereas there was not a statistically significant difference between those with the Hill-RBF3.0 and Kane. The absolute errors with the Hill-RBF3.0 and Kane formulas were smaller than that with the BUII formula, whereas there was not a statistically significant difference between the other formulas. The percentage within ±0.25 D with the Hill-RBF3.0 formula was larger than that with the BUII formula. The prediction accuracy using AI (Hill-RBF3.0 and Kane) showed excellent prediction accuracy. No significant difference was observed in the prediction accuracy between the Hill-RBF3.0 and Kane formulas.