Application of total keratometry in ten intraocular lens power calculation formulas in highly myopic eyes

The effect of patient age on some new and older IOL power calculation formulas

PURPOSE

To assess the accuracy of intraocular lens (IOL) power calculation in different age groups using various IOL calculation formulas.

METHODS

Data from 421 eyes of 421 patients ≥60 years old (ages: 60-69, n = 131; 70-74, n = 105; 75-84, n = 158 and ≥85, n = 27), who underwent uneventful cataract surgery with SN60WF IOL implantation at John A. Moran Eye Center, Salt Lake City, USA, were retrospectively obtained. The SD of the prediction error (PE), median and mean absolute PEs and the percentage of eyes within ±0.25, ±0.50, ±0.75 and ±1.00 D were calculated after constant optimizations for the following formulas: Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay 1, Kane, Radial Basis Function (RBF) 3.0 and SRK/T. Results were compared between the different age groups.

RESULTS

Predictability rates within 0.25D were lower for the eldest age group compared with the other groups using the EVO 2.0 (33% vs. 37%-53%, p = 0.045), Kane (26% vs. 35%-50%, p = 0.034) and SRK/T (22% vs. 31%-49%, p = 0.002). Higher median absolute refractive errors for all formulas were observed in the oldest group [range: 0.39 D (Haigis, Hoffer QSR)-0.48 D (Kane)], followed by the youngest group [range: 0.30 D (RBF 3.0)-0.39 D (Holladay 1, SRK/T)] but did not reach statistical significance. No significant differences between the groups in the distribution parameter were seen.

CONCLUSION

Current IOL power calculation formulas may have variable accuracy for different age groups. This should be taken into account when planning cataract surgery to improve refractive outcomes.

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.

Effect of Posterior Keratometry on the Accuracy of 10 Intraocular Lens Calculation Formulas: Standard Keratometry versus Total Keratometry

PURPOSE

To investigate the effect of posterior keratometry (PK) on the accuracy of 10 intraocular lens (IOL) power calculation formulas using standard keratometry (K) and total keratometry (TK).

METHODS

This is a retrospective consecutive case-series study. The IOL power was calculated using K and TK measured by IOLMaster 700 in 6 new-generation formulas (Barrett Universal II, Emmetropia Verifying Optical (EVO) 2.0, RBF Calculator 3.0, Hoffer QST, Kane, and Ladas Super Formula) and 4 traditional formulas (Haigis, Hoffer Q, Holladay 1, and SRK/T). The arithmetic prediction error (PE) and mean absolute PE (MAE) were evaluated. The locally-weighted scatterplot smoothing was performed to assess the relationship between PE and PK.

RESULTS

A total of 576 patients (576 eyes) who underwent cataract surgery were included. Compared with using K, all formulas using TK showed a hyperopic shift in the whole group. Specifically, for eyes with PK exceeding -5.90 D, all formulas using TK exhibited a hyperopic shift (all P < 0.001), while eyes with PK less than -5.90 D showed a myopic shift (all P < 0.001). The MAE of new-generation formulas calculated with TK and K showed no statistical differences, while the MAE of traditional formulas with TK was larger (TK: 0.34 ~ 0.43 D; K: 0.33 ~ 0.42 D, all P < 0.05).

CONCLUSIONS

The prediction bias of formulas with TK increased as PK deviated from -5.90 D. TK did not improve the prediction accuracy of new-generation formulas, and even performed worse in traditional formulas.

Accuracy of Modern Intraocular Lens Formulas in Highly Myopic Eyes Implanted With Plate-Haptic Intraocular Lenses

PURPOSE

To evaluate the predictive accuracy of modern intraocular lens (IOL) formulas and axial length (AL) adjusted traditional IOL formulas, including Wang-Koch and Cooke-modified AL (CMAL) method, in long eyes with plate-haptic IOLs, and to compare refractive prediction error variances with C-loop IOLs.

DESIGN

Retrospective consecutive case series study.

METHODS

Data from 391 eyes with Zeiss 509 M and 302 eyes with Alcon SN6CWS implants in highly myopic patients, following cataract surgery from January 2019 to November 2023, were collected. One eye per patient was selected. Predictive outcomes of 15 modern formulas (Barrett Universal II (BU II), Cooke K6 (K6), Emmetropia Verifying Optical (EVO) 2.0, Hoffer-QST, Kane, Karmona, Ladas AI, Naeser 2, Olsen, Pearl-DGS, Radial Basis Function (RBF) 3.0, T2, VRF-G, Zhu-Lu, and Z-Calc) and 4 traditional IOL formulas (Haigis, Hoffer Q, Holladay 1, and SRK/T) with AL adjusted methods, were evaluated. The mean prediction error, mean absolute prediction error (MAE), root-mean-square absolute prediction error (RMSAE) and the proportions of eyes with PEs within ±0.25 Diopter (D), ±0.50 D, ±0.75 D, and ±1.00 D were analyzed. Top 10 RMSAE-ranked formulas underwent further subgroup analysis based on AL, anterior chamber depth (ACD), and keratometry (K).

RESULTS

For the 509 M group, RMSAE ranking for the top 10 IOL formulas were the RBF 3.0 (0.432), Zhu-Lu (0.436), Olsen (0.436), EVO 2.0 (0.437), Pearl-DGS (0.447), K6 (0.452), VRF-G (0.454), Naeser 2 (0.464), Haigis-CMAL (0.465) and Karmona (0.477). Karmona and Naeser 2 showed poorer performance in the extremely long AL and steep K subgroups, respectively (p ≤ 0.042). Haigis-CMAL accuracy was significantly lower in shallow ACD and flat K subgroups (P ≤ .045). The SN6CWS group showed significantly lower MAE and RMSAE compared to the 509 M group for the BU II, EVO 2.0, Hoffer-QST, Kane, Pearl-DGS, and Zhu-Lu formulas (P ≤ .024).

CONCLUSIONS

In long eyes with plate-haptic IOLs, RBF 3.0 performed best, closely followed by Zhu-Lu, Olsen, and EVO 2.0; Karmona and Naeser 2 are discouraged for extreme AL and steep K conditions, respectively; Haigis-CMAL is not suggested for shallow ACD and flat K cases. Refractive outcomes in eyes implanted with a C-loop design IOL were more accurate than for those implanted with a plate-haptic design, for most tested formulas.

Differences Between Keratometry and Total Keratometry Measurements in a Large Dataset Obtained With a Modern Swept Source Optical Coherence Tomography Biometer

PURPOSE

This study aimed to explore the concept of total keratometry (TK) by analyzing extensive international datasets representing diverse ethnic backgrounds. The primary objective was to quantify the disparities between traditional keratometry (K) and TK values in normal eyes and assess their impact on intraocular lens (IOL) power calculations using various formulas.

DESIGN

Retrospective multicenter intra-instrument reliability analysis.

METHODS

The study involved the analysis of biometry data collected from ten international centers across Europe, the United States, and Asia. Corneal power was expressed as equivalent power and astigmatic vector components for both K and TK values. The study assessed the influence of these differences on IOL power calculations using different formulas. The results were analyzed and plotted using Bland-Altman and double angle plots.

RESULTS

The study encompassed a total of 116,982 measurements from 57,862 right eyes and 59,120 left eyes. The analysis revealed a high level of agreement between K and TK values, with 93.98% of eyes exhibiting an absolute difference of 0.25 D or less. Astigmatism vector differences exceeding 0.25 D and 0.50 D were observed in 39.43% and 1.08% of eyes, respectively.

CONCLUSIONS

This large-scale study underscores the similarity between mean K and TK values in healthy eyes, with rare clinical implications for IOL power calculation. Noteworthy differences were observed in astigmatism values between K and TK. Future investigations should delve into the practicality of TK values for astigmatism correction and their implications for surgical outcomes.

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.

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.