PURPOSE
To evaluate visual outcomes with particular attention to visual acuity of a segmental refractive multifocal intraocular lens, the ACUNEX VarioMax (Teleon Surgical).
METHODS
This prospective, non-randomized, non-comparative study was conducted at the Department of Ophthalmology, University Hospital, Goethe University, Frankfurt, Germany. Thirty-four eyes (17 patients) were included. Inclusion criteria were bilateral cataract, age older than 30 years, and predicted corneal astigmatism ≤0.75 diopters (D) postoperatively. Exclusion criteria were prior ocular surgeries, amblyopia, or potential postoperative corrected distance visual acuity worse than 0.3 logarithm of the minimum angle of resolution (logMAR). Uncorrected visual acuities for distance, intermediate, and near and distance-corrected visual acuities at 4 m, 80 cm and 40 cm, contrast sensitivity, defocus curve, and questionnaires on optical quality, quality of life, and spectacle independence were assessed after 3 months.
RESULTS
The mean monocular uncorrected distance visual acuity was 0.02 ± 0.09 logMAR at 4 m, uncorrected intermediate visual acuity was 0.15 ± 0.12 logMAR at 80 cm and uncorrected near visual acuity was 0.24 ± 0.10 logMAR at 40 cm. Defocus curve testing showed a distance-corrected binocular visual acuity range between 0.00 and -2.50 D (from -0.06 to 0.12 logMAR). Median contrast sensitivity under photopic and mesopic conditions was 1.48 ± 0.18 and 1.25 ± 0.29 logCS, respectively.
CONCLUSIONS
This segmental multifocal intraocular lens provides good visual acuity at all distances, particularly providing good intermediate visual acuity and depth of focus. It showed good contrast sensitivity and high spectacle independence with a high degree of patient satisfaction.
T. Kohnen, Jakob Wend, Zubeida H Omerovic et al.· Journal of refractive surger...· 0 citations
Abstract Purpose This study compared traditional statistical models with machine learning algorithms for predicting surgically induced astigmatism after cataract surgery, aiming to identify the most accurate and generalizable method among linear regression, regression trees, random forests, and neural networks. Methods Retrospective analysis was performed on 321 eyes (321 patients) undergoing phacoemulsification at a tertiary center. Data were standardized and surgically induced astigmatism was modeled using three vector-based outcomes: KEQ.post (keratometric equivalent power), KAST0.post (horizontal astigmatism component), and KAST45.post (oblique component). Preoperative variables served as predictors. Data were split into training (60%) and test (40%) sets. Four predictive models were developed and evaluated on unseen data. Performance was assessed using mean squared prediction error, and variable importance analyses identified key predictors. Results Linear regression achieved the best out-of-sample performance across all outcomes (e.g. KEQ.post mean squared prediction error = 0.043). Tree-based models performed slightly worse, while neural networks showed substantial overfitting with markedly higher test errors. Preoperative astigmatism and corneal radii were the strongest predictors. Conclusion KAST0 and KAST45 were not predictably modeled, whereas changes in KEQ.post were. Multivariable linear regression provided the most accurate and reliable predictions, while more complex machine-learning models (especially neural networks) overfit the limited dataset and offered no clinical advantage. The relationship between preoperative biometrics and postoperative equivalent power appears predominantly linear, though larger datasets may enhance machine-learning performance.
Amanda Pan, K. P. Kaiser, Stefan Raidl et al.· Current Eye Research· 0 citations