Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length

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 20 Intraocular Lens Power Calculation Formulas in Medium-Long Eyes

INTRODUCTION

The aim of this work is to compare 20 intraocular lens (IOL) power calculation formulas in medium-long eyes (24.50-25.99 mm) in terms of root mean square absolute error (RMSAE), median absolute error (MedAE), and percentage of eyes with prediction error (PE) within ± 0.50 D.

METHODS

The data of patients who underwent uneventful phacoemulsification between January 2017 and September 2023 were reviewed. Pre-surgery IOL power was calculated using Holladay1, SRK/T, Hoffer Q, Holladay 2, and Haigis. Three months after phacoemulsification, refraction was measured. Post-surgery IOL power calculations were performed utilizing the following formulas: Barrett Universal II, Kane, K6, Olsen (OLCR), Olsen (standalone), PEARL-DGS, Ladas Super Formula AI (LSF AI), T2, EVO, VRF, Hoffer QST, Castrop, VRF-G, Karmona, and Naeser 2. RMSAE, MedAE, and percentage of eyes with PE within ± 0.25 D, ± 0.50 D, ± 0.75 D and ± 1.00 were calculated.

RESULTS

One hundred twenty-four eyes with axial length ranges between 24.52 and 25.97 mm were studied. The SRK/T formula yielded the lowest RMSAE (0.206) just before Holladay 1 (0.260) and T2 (0.261). In terms of MedAE, the best outcome was obtained by SRK/T (0.12) followed by Barrett Universal II (0.15) and LSF AI (0.15). The highest percentage of eyes with prediction error within ± 0.50 D was achieved by SRK/T, T2, and Holladay 1 (97.58, 93.55, and 93.55%, respectively).

CONCLUSIONS

Third-generation formulas (SRK/T, Holladay 1) provided highly accurate outcomes in medium-long eyes and still can be wildly used to calculate IOL power.

Systematic evaluation of machine learning-enhanced trifocal IOL power selection for axial myopia cataract patients

PURPOSE

This study aimed to evaluate and optimize intraocular lens (IOL) power selection for cataract patients with high axial myopia receiving trifocal IOLs.

DESIGN

A multi-center, retrospective observational case series was conducted. Patients having an axial length ≥26 mm and undergoing cataract surgery with trifocal IOL implanted were studied.

METHODS

Preoperative biometric and postoperative outcome data from 139 eyes were collected to train and test various machine learning (ML) models (support vector machine, linear regression, and stacking regressor) using five-fold cross-validation. The models' performance was further validated externally using data from 48 eyes enrolled from other hospitals. Performance of seven IOL calculation formulas (BUII, Kane, EVO, K6, DGS, Holladay I, and SRK/T) were examined with and without ML models.

RESULTS

The results of cross-validation revealed improvements across all IOL calculation formulas, especially for K6 and Holladay I. The model increased the percentage of eyes with a prediction error (PE) within ±0.50 D from 71.94% to 79.14% for K6, and from 35.25% to 51.80% for Holladay I. In external validation involving 48 patients from other centers, six out of seven formulas demonstrated a reduction in the mean absolute error (MAE). K6's PE within ±0.50 D improved from 62.50% to 77.08%, and Holladay I from 16.67% to 58.33%.

CONCLUSIONS

In this study, we conducted a comprehensive evaluation of seven IOL power calculation formulas in high axial myopia cases and explored the effectiveness of the Stacking Regressor model in augmenting their accuracy. Of these formulas, K6 and Holladay I exhibited the most significant improvements, suggesting that integrating ML may have varying levels of effectiveness across different formulas but holds substantial promise in improving the predictability of IOL power calculations in patients with long eyes.

Comparison of the accuracy of 9 intraocular lens power calculation formulas after SMILE in Chinese myopic eyes

As of 2021, over 2.8 million small-incision lenticule extraction (SMILE) procedures have been performed in China. However, knowledge regarding the selection of intraocular lens (IOL) power calculation formula for post-SMILE cataract patients remains limited. This study included 52 eyes of 26 myopic patients from northern China who underwent SMILE at Tianjin Eye Hospital from September 2022 to February 2023 to investigate the suitability of multiple IOL calculation formulas in post-SMILE patients using a theoretical surgical model. We compared the postoperative results obtained from three artificial intelligence (AI)-based formulas and six conventional formulas provided by the American Society of Cataract and Refractive Surgery (ASCRS). These formulas were applied to calculate IOL power using both total keratometry (TK) and keratometry (K) values, and the results were compared to the preoperative results obtained from the Barrett Universal II (BUII) formula for the SMILE patients. Among the evaluated formulas, the results obtained from the Emmetropia Verifying Optical 2.0 Formula with TK (EVO-TK) (0.40 ± 0.29 D, range 0-1.23 D), Barrett True K with K formula (BTK-K, 0.41 ± 0.26 D, range 0.01-1.19 D), and Masket with K formula (Masket-K, 0.44 ± 0.33 D, range 0.02-1.39 D) demonstrated the closest proximity to BUII. Notably, the highest proportion of prediction errors within 0.5 D was observed with the BTK-K (71.15%), EVO-TK (69.23%), and Masket-K (67.31%), with the BTK-K showing a significantly higher proportion than the Masket-K (p < 0.001). Our research indicates that in post-SMILE patients, the EVO-TK, BTK-K, and Masket-K may yield more accurate calculation results. At their current stage in development, AI-based formulas do not demonstrate significant advantages over conventional formulas. However, the application of historical data can enhance the performance of these formulas.

Using the First-Eye Back-Calculated Effective Lens Position to Improve Refractive Outcome of the Second Eye

The present study is a retrospective, monocentric case series that aims to compare the second-eye IOL power calculation precision using the back-calculated lens position (LP) as a lens position predictor versus using a predetermined correction factor (CF) for thin- and thick-lens IOL calculation formulas. A set of 878 eyes from 439 patients implanted with Finevision IOLs (BVI PhysIOL, Liège, Belgium) with both operated eyes was used as a training set to create Haigis-LP and PEARL-LP formulas, using the back-calculated lens position of the contralateral eye as an effective lens position (ELP) predictor. Haigis-CF, Barrett-CF, and PEARL-CF formulas using an optimized correction factor based on the prediction error of the first eye were also designed. A different set of 1500 eyes from 1500 patients operated in the same center was used to compare the basal and enhanced formula performances. The IOL power calculation for the second eye was significantly enhanced by adapting the formulas using the back-calculated ELP of the first eye or by using a correction factor based on the prediction error of the first eye, the latter giving slightly higher precision. A decrease in the mean absolute error of 0.043D was observed between the basal PEARL and the PEARL-CF formula (p < 0.001). The optimal correction factor was close to 60% of the first-eye prediction error for every formula. A fixed correction factor of 60% of the postoperative refractive error of the first operated eye improves the second-eye refractive outcome better than the methods based on the first eye’s effective lens position back-calculation. A significant interocular biometric dissimilarity precludes the enhancement of the second-eye IOL power calculation according to the first-eye results.