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Moderator
Seth M. Pantanelli, MD, MS, ABO
Panelists
Janusz Skrzypecki, MD, PhD
Viewing Papers
Expand a paper title to the right to view the paper abstract and authors. Use the video link to jump to that poster in the session.
Presenting Author
Gizem Taskin, MD, FEBO
Co-Authors
Gülay Yalç?nkaya çak?r (FEBO), Ahmet Kirgiz (FEBO), Nilay kandemir Besek (MD), Sibel Ahmet (FEBO, FRCSEd, MD), Burcu Kemer Atik (MD)
Purpose
To compare the reliability and accuracy of three optical biometry devices: AL-Scan (Nidek), Lenstar LS 900 (Haag-Streit), and Aladdin (Topcon) in intraocular lens (IOL) power calculation and refractive error prediction in patients undergoing uncomplicated phacoemulsification surgery.
Methods
Patients who underwent uncomplicated phacoemulsification surgery between January 1 and 31, 2025, in our tertiary eye center were evaluated. Preoperative measurements of axial length (AL), flat (K1), steep (K2), and mean keratometry (K-mean) obtained using AL-Scan, Lenstar LS 900, and Aladdin were compared, along with the mean absolute error (MAE) values of the three devices. Best-corrected visual acuity and refraction values were recorded preoperatively and at 1 month postoperatively.
Results
Thirty-two eyes of 32 patients were included. AL differed significantly among devices (p=0.02); Aladdin values were higher than Lenstar LS 900 (p=0.01, Bonferroni). K-mean, K1, and K2 measured with AL-Scan were lower than both others (all p<0.001, Bonferroni). A proportional bias existed between Lenstar and Aladdin for AL (?=0.018, p=0.017, R�=0.176), with differences increasing at longer AL, while no bias was found for keratometry (K1, K2, or K-mean). Postoperative MAE also differed (p=0.02), being lower with AL-Scan versus Lenstar (p=0.03, Bonferroni). The proportion of eyes within ±0.25 D of predicted error was 56.3% for AL-Scan, 40.6% for Lenstar and 50.0% for Aladdin (Q=6.33, p=0.04).
Conclusion
AL-Scan provided more predictable results for IOL power selection compared to Lenstar LS 900, while showing comparable outcomes with Aladdin. Differences in AL and keratometry measurements across the three devices may influence IOL power selection and refractive outcomes.
Presenting Author
Li Wang, MD, PhD
Co-Authors
Xuesen Cheng (MD, PhD), Francisco Mendes (MD), Kusona Fortingo (BSc), Jay Jaber (None), Allison Chen (MD, MPH), Mitchell Weikert (MD, MS), Douglas Koch (MD)
Purpose
In some eyes with IOL exchange, the IOL is placed in the bag with anterior optic capture or in the sulcus with posterior optic capture. It is well known that, when the IOL is placed in the sulcus, IOL power adjustment is required. In this study, we investigated the impact of optic capture on refractive prediction errors (PEs).
Methods
The Barrett Rx formula was designed for eyes with IOL exchange. We proposed the MR-biometry approach, which incorporates data from the existing IOL (model and power), pre-exchange biometry, and manifest refraction with the original IOL. Both methods are for in-the-bag IOL calculation. IOL power calculations were performed using these two methods in eyes with IOLs in the bag with anterior optic capture (AOC) and sulcus with posterior optic capture (POC). PEs were calculated as the postoperative refraction minus the predicted refraction with these two methods. To evaluate the association between the implanted IOL powers and PEs, bivariate correlation analysis was performed.
Results
The ranges of IOL powers were 14 - 23 D in the AOC group, and 7 - 25 D in the POC group. With the MR-biometry and Barrett Rx formulas, respectively, the mean PEs were -0.20 D and -0.94 D in the AOC group (n=19), and -0.08 D and -0.71 D in the POC group (n=37); the correlation coefficient values were -0.429 (P=0.067) and -0.495 (P=0.031) in the AOC group, and -0.010 (P=0.953) and -0.029 (P=0.863) in the POC group.
Conclusion
The preliminary results showed that there were moderate negative correlations between PEs and IOL powers in eyes with AOC, indicating that higher IOL powers produced PEs more myopic than predicted. Weak correlations between PEs and IOL powers were found in the POC group. More eyes will be enrolled, and results will be updated.
Presenting Author
Douglas D. Koch, MD
Co-Authors
Xuesen Cheng (MD, PhD), Francisco Mendes (MD), Kusona Fortingo (BSc), Jay Jaber (None), Allison Chen (MD, MPH), Mitchell Weikert (MD, MS), Li Wang (MD, PhD)
Purpose
To introduce a novel, in-house formula ("MR-biometry approach") for calculating intraocular lens (IOL) power when performing IOL exchange and to compare its refractive prediction accuracy with that of the Barrett Rx formula.
Methods
IOL power calculations in eyes without previous corneal refractive surgery were performed using two methods: 1) the MR-biometry approach, which incorporates data from the existing IOL (model and power), pre-exchange biometry, and manifest refraction with the original IOL; and 2) the Barrett Rx formula. Refractive prediction errors (PEs) were calculated as the difference between actual postoperative refraction and predicted refraction with these two formulas. The standard deviations (SD), root mean square absolute errors (RMSAE), and percentage of eyes within ±0.25 D and ±0.5 D of PEs were analyzed.
Results
A total of 170 eyes from 168 patients were included. The MR-Biometry formula demonstrated consistently lower RMSAE compared with the Barrett Rx formula in all positioning subgroups: bag-to-bag exchange (0.476 D vs. 0.585 D), bag-to-bag exchange with anterior optic capture (0.606 D vs. 1.17 D), and sulcus placement with posterior optic capture (0.812 D vs. 1.10 D). The MR-Biometry formula produced significantly higher percentages of eyes within ±0.25 D of PEs in all 3 subgroups, and within ±0.5 D of PEs in subgroups with anterior and posterior optic captures.
Conclusion
The MR-Biometry formula demonstrated superior refractive accuracy compared with the Barrett Rx formula in eyes undergoing IOL exchange. These findings support its potential as a valuable tool in complex IOL exchange planning.
Presenting Author
Kusona Fortingo, BSc
Co-Authors
Douglas Koch (MD), Xuesen Cheng (MD, PhD), Li Wang (MD, PhD), Mitchell Weikert (MD, MS), Francisco Mendes (MD), Allison Chen (MD, MPH), Karen Asfar (MD)
Purpose
To evaluate the accuracy of corneal astigmatism prediction using back-calculated corneal astigmatism derived from postoperative refraction obtained subjectively or with a Shack-Hartmann wavefront sensor.
Methods
Corneal astigmatism will be measured before cataract surgery using five devices. Back-calculated corneal astigmatism will be calculated as the difference between the postoperative refraction and the effective toric IOL power, both corrected to the corneal plane. Postoperative refraction will be determined either by subjective manifest refraction or objectively with a high-resolution Shack-Hartmann wavefront sensor. Eyes implanted with both spherical and toric IOLs will be included in the analysis. The predictive accuracy of each device will be assessed by comparing preoperative keratometric values with back-calculated corneal astigmatism.
Results
TBA
Conclusion
TBA
Presenting Author
Naren Shetty, MS, PhD
Co-Authors
Abhijit Roy (PhD), Tejal Sj (MS), Amulya Punati (MBBS, MS)
Purpose
To develop an AI-based model for determining ocular dominance using biometry parameters, eliminating the need for traditional dominance tests, with potential applications in monovision planning.
Methods
Motor dominance was assessed in 240 patients aged 20-40 years using the diamond test for dominance. Patients were categorized into Group 1 (right-eye dominant) and Group 2 (left-eye dominant). All subjects underwent biometry with the IOLMaster 700 (Carl Zeiss Meditech, Inc). Biometric parameters were compared between groups and used to train an AI model to predict ocular dominance.
Results
Among 240 patients, the neural network achieved the best performance (AUC 0.629, accuracy 62.5%), correctly classifying 61.7% of left-eye dominant and 63.3% of right-eye dominant cases. Key predictive parameters included white to white (WTW), Delta K, K2 (RE & LE), and Axial length, enabling ocular dominance prediction from biometry alone.
Conclusion
AI-driven analysis of biometric parameters offers a reliable, non-invasive alternative to conventional dominance tests, facilitating efficient and objective planning for monovision strategies in refractive and cataract surgery.
Presenting Author
Matteo Piovella, MD
Co-Authors
Barbara Kusa (MD)
Purpose
Refractive cataract surgery is based on the possibility to achieve a good postoperative refractive outcome within a range of 0.50 diopters. Advanced biometry is effective when detects the proper cornea curvature to decrease halos and glare. Blephex LipiFlow and ILux adopted before cataract surgery help to detect the lens precise power
Methods
refractive cataract surgery is based on to improve tear film quality when it has to approach advanced technology IOLs implantation. Not considering dry eye or MGD could penalize the refractive quality of the visual outcomesTo evaluate three consecutive treatments, Blephex for Lid Scrub(BlephEx, Brentwood TN) LipiFlow (J&J Santa Ana Ca) ILux (Alcon Fort Worth Texas) for the thermal pulsation treatment of Meibomian Gland Dysfunction(MGD) in evaporative Dry Eye to improve quality of tear film and of the surface of the cornea to get precise biometryMethodsSince 2017
Results
378 patients (mean age 66.58 ±11.55 years) were treated for MGD. And Evaporative Dry Eye. They received a LipiFlow treatment to remove obstructions and restore meibomian gland function.123 of these patients received also Blephex treatment immediately before since September 2019.65 patients since September 2023 adopted also ILux treatment to complete the MAPHRY protocol based on three physical treatmentsResults:Postop quality of vision improved in all patients, and regular cornea surface provided more precise and stable biometry results. The adoption of blephex lipiflow and ILux treatments provided 97% of eyes inside the planned refractive postoperative outcome
Conclusion
outcomeConclusionThese treatments have a priority role in adopting implants to correct refractive defects and treat presbyopia in cataract patients
Presenting Author
Vaishali Vasavada, MS
Co-Authors
Lajja Shastri (MS), Shail Vasavada (DNB, FRCS), Vandana Nath (MS, DO), Abhay Vasavada (MS, FRCS), Jash Bavishi (MS)
Purpose
To evaluate the performance of the Barrett Universal formula with segmented axial length (AL) compared to traditional composite AL
Methods
Consecutive patients who underwent uncomplicated cataract surgery. The composite axial length (AL) was measured with a swept-source optical coherence tomography (SS-OCT) biometer using a mean refractive index. The segmented AL was calculated by summing the geometric lengths of the ocular segments (cornea, aqueous, lens, and vitreous) using multiple specific refractive indices based on the data obtained by the SS-OCT-based biometer. Manifest refraction was measured at three months postoperatively, and the prediction error of refraction was calculated with two ALs for each formula. Eyes were classified based
Results
The study included 50 eyes of 50 patients. The segmented AL was shorter than the composite AL. The study is ongoing and final results will be updated at the time of presentation.
Conclusion
Based on preliminary results, it appears that the TAL enhances the accuracy of the Barrett formula.
Presenting Author
Lisa M. Nijm, MD, JD
Purpose
The presence of hyperosmolarity pre- and post-cataract surgery can result in refractive error and patient dissatisfaction, despite target refraction being achieved. The aim of this study is to determine rates of hyperosmolarity pre- and post-surgery, and investigate measurement methods to detect this in clinical practice.
Methods
The prevalence of hyperosmolarity (PS) and its effect on light scatter (LSS) were investigated in two prospective, observational studies. Patients were assessed for osmolarity using the ScoutPro system using a cut-off of ≥315 mOsm/L. Patients in the LSS had normal osmolarity (n=10) or hyperosmolarity (n=11). Eighty patients were included in the PS, 44% of which had hyperosmolarity. Osmolarity, symptom severity, tear film break-up time (TBUT), and meibomian gland disease (MGD) were assessed at baseline in both studies. Contiguous, 20-second ocular scatter index (OSI) scans were also performed in the LSS. Patients were followed up at 30 days (PS) and 90 days (LSS) post-surgery.
Results
In the PS, of the 56% of patients who had normal osmolarity pre-surgery, 32% became hyperosmolar post-surgery. Of the 44% of patients who were hyperosmolar pre-surgery, 52% achieved normal osmolarity post-surgery, despite topical corticosteroid, NSAID, antibacterial, and artificial tear treatment during the postoperative period. In the LSS, variation in light scatter as measured by OSI correlated with osmolarity status at baseline (mean±SD OSI: 0.33±0.11 in normal osmolarity group, 0.65±0.30 in hyperosmolar group; p<0.005). OSI did not correlate with staining (p=0.891), TBUT (p=0.749), symptoms (p=0.719), or MGD status (p=0.852) when patients were stratified at baseline by these measures.
Conclusion
These studies demonstrate hyperosmolarity is common pre- (44%) and post- (40%) cataract surgery. They also demonstrate testing for and stratifying by osmolarity status is the only way to detect the source of light scatter in these patients, which is of significance for enhancement of visual outcomes and patient satisfaction.
Presenting Author
Edward Tran, MD
Co-Authors
Iqbal Ike Ahmed (MD, FRCSC), Jia Yue You (MD, FRCSC)
Purpose
To evaluate the predictive refractive accuracy of intraoperative aberrometry (IA) with the Optiwave Refractive Analysis (ORA) system in intraocular lens (IOL) exchange surgery and compare its performance with traditional IOL formulas and preoperative biometric data.
Methods
This is a single-center, retrospective chart review of intracapsular IOL exchanges between 2021 and 2025, where ORA IA was used to recommend an IOL power. Predicted refractive error and IOL power recommendations of ORA IA were compared with Barrett Universal II (using phakic biometry), SRK/T (using pseudophakic biometry), Barrett Rx, and surgeon clinical judgment. The primary outcome was the percentage of eyes within ±0.25 diopters (D), ±0.50 D, ±0.75 D, and ±1.00 D of the predicted refractive error. Secondary outcomes included IOL agreement between the different methods, surgeon selection of ORA IA recommended IOL power, prediction error metrics, visual acuity, and complications.
Results
Preliminary analysis was conducted (N=30). Evaluation of predicted error indicated that 62% of eyes were within ±0.50 D of the target refraction for ORA IA, 67% for Barrett Universal II (BUII), 62% for SRK/T, and 43% for Barrett Rx (P = 0.24). The mean absolute error for ORA IA was 0.48 ± 0.30, for BUII was 0.53 ± 0.38, for SRK/T was 0.52 ± 0.53, and for Barrett Rx was 0.96 ± 0.91 (P = 0.14). In 27% of cases, the operating surgeon changed the initial IOL choice based on ORA IA recommendation. In 53% of cases, the final implanted IOL matched the ORA IA recommendation. At postoperative ?1 month, mean UDVA logMAR was 0.16 ± 0.15 pre-operatively and 0.13 ± 0.10 post-operatively (P = 0.38).
Conclusion
ORA IA is a useful tool to assist the selection of intraocular lens power for intracapsular IOL exchanges.
Presenting Author
Nahyun Park, MD
Co-Authors
Chung Min Lee (MD), Yeaeun Lee (MD), Jeewon Han (None), Kyu Sang Eah (MD), Hoseok Chung (MD), Jae Yong Kim (MD, PhD), Hun Lee (MD, PhD), Kyung Min Koh (MD, PhD, MBA), David Cooke (MD), Cecily Kaufmann (BSN)
Purpose
To evaluate the effectiveness of prediction modifications (PMOD) in improving refractive accuracy of modern intraocular lens (IOL) formulas in high-power TECNIS IOLs.
Methods
This retrospective observation study included 645 eyes (497 patients) implanted with TECNIS ZCB00 or ICB00 IOLs. Eyes were stratified into central power (17.5-23.5 D) and high-power (>23.5 D) groups. Formula constants were optimized using the central power group and applied to both groups. PMOD, based on prior large-scale regression data, were applied to the high-power group. Twelve IOL power formulas were evaluated before and after PMOD application. Prediction errors were analyzed using mean refractive error (MRE), mean absolute error (MAE), and root mean square absolute error (RMSAE) with the Wilcox-Holladay-Wang-Koch (WHWK) method.
Results
In the high-power group, eyes exhibited significantly shorter axial length, shallower anterior chamber depth, and greater lens thickness (all P<0.001). All formulas including Barrett Universal II, Cooke K6, EVO 2.0, Haigis, Hoffer QST, Holladay 1, Holladay 2 with nonlinear regression (H2 NLR), Kane, Olsen, PEARL-DGS, SRK/T, and T2 achieved -0.01-0.01 D MRE after optimization in the central power (17.5-23.5 D) group. In the high-power (>23.5 D) group, PMOD effectively corrected the myopic shift, significantly reducing MRE, MAE, and RMSAE across all formulas (all P<0.05), except for MAE in PEARL-DGS (P=0.050).
Conclusion
PMOD, a simple diopter-specific adjustment, effectively improved refractive prediction accuracy and reduced residual systematic errors that persisted in high-power IOLs across formulas, even after applying constants optimized in the central power group.
Presenting Author
Danmin Cao, PhD, MD
Purpose
To characterize the three-dimensional lens parameters in age-related cataract patients and examine their associations with age, sex, and ocular biometric parameters.
Methods
In this cross-sectional study, we consecutively enrolled patients aged≥40 years who underwent cataract surgery. Three-dimensional lens parameters were acquired using swept-source anterior segment optical coherence tomography (SS-AS-OCT), including: anterior lens surface curvature radius (RAL), posterior lens surface curvature radius (RPL), lens thickness (LT), anterior lens thickness (LTa), lens diameter (LD), and lens volume (LV). Correlational analyses were performed between these parameters and patients' age, sex, and ocular biometric parameters (axial length [AL], mean keratometry [Km], anterior chamber depth [ACD], and horizontal corneal diameter [white-to-white, WTW])
Results
356 (140M/216F); age 66.48±9.60y. Lens params (mean±SD): RAL=9.18±1.32, RPL=5.52±0.80, LT=4.49±0.46, LTa=1.49±0.34, LD=9.67±0.95, LV=224.71±56.98mm³. Abnormal LT (≤4.0/>5.0):14.04%/12.92%. No sex diff (p>0.05). RAL neg age (R=-0.142, p=0.007; -0.019/y). LT (R=0.279, p<0.001; +0.013/y), LTa (R=0.194, p<0.001; +0.007/y), LV (R=0.177, p=0.001; +1.052/y) pos age. LT: +0.022/y (≤60y) vs +0.010/y (>60y). No age-RPL/LD (p>0.05). Inter-param: RAL neg LT/LTa, pos RPL/LD; RPL/LT/LTa/LD/LV pos (p<0.05). Ocular: RAL pos ACD/AL/WTW, neg Km; RPL pos AL; LT/LTa/LD/LV neg ACD, LD/LV pos AL (p<0.05). Segmented reg: AL non-linear RAL (24.50), LT (24.76), LTa (24.40) inflection.
Conclusion
Cataract patients ≥40y: age-related increases in anterior lens convexity, thickness, volume; faster progression before 60y. Shallower anterior chambers linked to greater lens convexity, thickness, volume. Eyes with axial length outside normal range: anterior lens convexity/thickness increased with more deviation from normal thresholds.
Presenting Author
Mitchell P. Weikert, MD, MS
Co-Authors
Francisco Mendes (MD), Serra Tuzun (BSc), Li Wang (MD, PhD), Michael Snyder (MD)
Purpose
To compare the predictive accuracy of 7 modern IOL calculations formulas in eyes following cataract surgery with intracapsular implantation of a capsular tension ring (CTR), intraocular lens (IOL), and iris prosthesis.
Methods
Consecutive cataract surgeries by a single surgeon (MES) with intracapsular CTR, IOL, & HumanOptics Artificial Iris implantation were analyzed. Prediction errors (PE) for the Barrett Universal II, Cooke K6, EVO, Hill-RBF, Hoffer QST, Kane, & Pearl DGS were calculated. PE standard deviations (SD) & root mean square absolute errors (RMSAE) were compared via heteroscedastic analysis. The mean & median PEs (MPE & MedPE), mean & median absolute errors (MAE & MedAE), & % of eyes within ±0.25, ±0.50, & ±1.00 diopters (D) of target were also compared using bootstrap-t or Wilcox signed rank tests with Holm correction. Astigmatic PEs for toric & non-toric IOLs were examined using double-angle plots.
Results
Ninety-five eyes of 95 patients were included in the analysis. The mean PEs were all hyperopic and ranged from +0.88 to 1.09 D, all which were statistically different from zero. The PE SDs ranged from 0.71 to 0.75 D with no statistically significant differences between formulas. There were also no statistically significant differences between formulas for % eyes within ±0.25, ±0.50, and ±1.00 D of target. Both the non-toric and toric IOL groups showed statistically significant reductions in mean refractive astigmatism (1.28 to 0.56 D and 2.40 to 0.72 D, respectively).
Conclusion
The 7 formulas had similar accuracy for eyes following intracapsular implantation of a CTR, IOL, and HumanOptics Artificial Iris. All PEs were hyperopic in the range of approximately +1.0 D, likely due to posterior displacement of the IOL by the iris prosthesis. Both toric and non-toric IOLs showed significant reductions in refractive astigmatism.
