Search Results (10)

Background/Objectives: The purpose of this study was to investigate the variation in subjective manifest refraction measures in a patient cohort screened for myopic refractive surgery. Methods: In this retrospective non-randomised cross-sectional single-centre study, we evaluated a dataset containing sequences of three refraction measurements performed by four experienced optometrists in 175 eyes screened for refractive corneal or lens surgery for myopia or myopic astigmatism. Refraction was converted from sphere (SPH), cylinder (CYL) and axis to power vector components (spherical equivalent SEQ and cylinder projections C0 and C45). The mean power vectors of the three repeat measurements (MEAN) and the deviations (DEV) of the repeat measurements from the MEAN were evaluated. Results: MEAN values for SPH/CYL/SEQ/C0/C45 were −5.93/0.99/−5.44/−0.47/0.02 D and the corresponding standard deviations were 0.20/0.16/0.17/0.17/0.16 D. DEV of both SEQ and CYL correlated significantly with patient age (Spearman R = 0.16 and 0.20). DEV of CYL correlated with mean (myopic) SEQ (R = −0.22) and CYL (R = 0.27) whereas DEV of SEQ showed no significant correlation with mean SEQ or CYL. Conclusions: The variation in subjective manifest refraction with repeat measurements is in a range of ±0.16 to ±0.20 D for SPH, CYL and the power vector components SEQ, C0 and C45. If reliable subjective refraction measurements are mandatory, e.g., for planning refractive surgery procedures or for formula constant optimisation, repeat refractometry measures could help to ensure representative data and to estimate the intraindividual variations. Full article

Background/Objectives: Toric intraocular lens (IOL) implantation is the standard approach for correcting corneal astigmatism during cataract surgery and refractive lens exchange (RLE). Evidence on outcomes in eyes with high corneal astigmatism (≥2.00 diopters, D), particularly in heterogeneous real-world settings, remains limited. This study evaluated visual, refractive, and astigmatic vector outcomes of toric IOL implantation in a consecutive high-astigmatism cohort and investigated predictors of residual astigmatic error. Methods: This single-center, single-surgeon retrospective analysis of prospectively collected data included 161 eyes (118 patients) with preoperative corneal astigmatism ≥ 2.00 D undergoing cataract surgery or RLE with toric IOL implantation (June 2023–December 2025). Primary outcomes at one month included visual acuity, manifest refraction, and Alpins vector analysis at the corneal plane. Secondary analyses comprised refractive stability assessment (n = 75 eyes, median seven months), comparison of astigmatic outcomes between emmetropia-targeted and intentional myopia-targeted eyes, and multivariate regression of predictors of residual astigmatic error. Results: Mean postoperative UDVA and CDVA were 0.19 ± 0.24 and 0.09 ± 0.15 logMAR, respectively. Spherical equivalent prediction error was −0.19 ± 0.42 D (69.6% within ±0.50 D of target). Mean residual cylinder was 0.52 ± 0.49 D; 62% and 88.8% of eyes achieved ≤0.50 D and ≤1.00 D, respectively. Vector analysis demonstrated a mean difference vector of 0.53 ± 0.44 D, a correction index of 1.04 ± 0.20, and near-zero centroid deviation (0.03 D @ 43°), indicating the absence of systematic directional prediction error. Refractive outcomes were stable at medium-term follow-up. Astigmatic correction accuracy was equivalent between emmetropia-targeted and intentional myopia-targeted eyes (p > 0.05 for all primary metrics). Multivariate regression identified IOL cylinder power (β = 0.051, p = 0.031) and oblique astigmatism orientation (β = 0.299 vs. WTR, p = 0.032) as independent predictors of greater residual astigmatic error. No sight-threatening complications occurred. Conclusions: Toric IOL implantation provides safe, predictable, and stable correction of high corneal astigmatism in a real-world mixed cohort. Astigmatic accuracy is maintained regardless of intended spherical refractive strategy, supporting the use of toric IOLs in highly myopic patients targeted for residual myopia. Oblique astigmatism orientation is an independent predictor of reduced correction accuracy, consistent with known limitations of current toric calculators for this meridian. Full article

Background and Objectives: Accurate anterior segment measurements are central to intraocular lens (IOL) power calculation and toric planning, yet different optical platforms may yield non-interchangeable values. This study compared keratometry, astigmatism metrics, and ocular biometry obtained with a swept-source OCT biometer (Argos), an optical low-coherence interferometry biometer (Aladdin), and a combined Scheimpflug–Placido topographer (Schwind Sirius). Methods: This is a retrospective observational study (January 2022–June 2024) including eyes undergoing uncomplicated cataract surgery. All eyes were measured in a single session by one examiner. Outcomes included K1, K2, cylinder, astigmatism axis (degrees; device-reported corneal cylinder axis, labeled “Powerful Angle” in the Sirius export), vector components (J0 and J45), and—where available—lens thickness (LT), axial length (AL), anterior chamber depth (ACD), white-to-white (WTW) distance, and central corneal thickness (CCT). Friedman tests assessed 3-device differences, and pairwise comparisons were evaluated using Wilcoxon signed-rank tests (paired data). Results: A total of 170 eyes (102 patients) were analyzed (mean age: 69.12 ± 10.26 years). Significant inter-device differences were detected for K1 (Argos: 43.45 ± 1.64 D; Aladdin: 43.41 ± 1.70 D; overall: p < 0.001; Argos vs. Aladdin: p = 0.019), K2 (Argos: 44.45 ± 1.67 D; Aladdin: 44.34 ± 1.71 D; overall and pairwise: p < 0.001), and cylinder (Argos: −0.83 ± 0.74 D, Aladdin: −0.77 ± 0.76 D; Sirius: −0.68 ± 0.75 D; overall: p < 0.001). “Powerful Angle” differed across devices (p = 0.003) but not between Argos and Aladdin (p = 0.512). J0 (p = 0.277) and J45 (p = 0.084) did not differ significantly. Argos reported higher ACD (3.19 ± 0.42 vs. 3.13 ± 0.41 mm, p < 0.001) and WTW (11.95 ± 0.42 vs. 11.65 ± 0.39 mm, p < 0.001) values than Aladdin. CCT was similar between Aladdin and Sirius (540.27 ± 33.44 vs. 540.47 ± 33.78 µm, p = 0.169). Conclusions: Several keratometric and biometric parameters differed significantly by device, indicating limited interchangeability—particularly relevant for toric and premium IOL planning—while vector astigmatism components and CCT showed better agreement. Full article

Background and Objectives: To evaluate the magnitude, axis and age-related changes in corneal astigmatism in patients before cataract surgery. Materials and Methods: In this retrospective, cross-sectional, and observational study, data from 2152 eyes that underwent phacoemulsification were evaluated. Keratometric values were obtained using the IOL Master 500 device. The frequency, magnitude and axis of corneal astigmatism were determined. The astigmatism axis was categorized as with the rule (WTR), against the rule (ATR), and oblique astigmatism. Quantitative analysis was performed using the power vector method (J0 and J45). The distribution and characteristics of corneal astigmatism data according to age were analyzed. Results: The mean age of the patients was 70.56 ± 8.88 years (range 40–94 years) and 1010 (46.9%) were males. Mean corneal astigmatism, J0 and J45 values were 0.96 ± 0.72, 0.05 ± 0.51, 0.01 ± 0.30 diopters (D), respectively. The most common range of magnitudes was 0.50–0.99 D with 38.8%, followed by <0.50 D (25.3%), 1.00–1.49 D (20.3%), and 1.50–1.99 D (8.7%). The cubic regression curve showed a U-shaped nonlinear relationship between age and corneal astigmatism (p < 0.001). The most common type of astigmatism was WTR with 43.4%, followed by ATR with 37.5% and oblique astigmatism with 19.1%. With the increase in age, the astigmatism axis gradually changed from WTR to ATR. There was a linear trend in the rate of these types of astigmatism across age groups (p < 0.05). Additionally, in patients under 65 years of age, WTR astigmatism was negatively correlated with age, while in patients 65 years of age and older, ATR astigmatism was positively correlated with age (r = −0.217, p < 0.001; r = 0.153, p < 0.001, respectively). Linear regression analyses revealed that the J0 value decreased significantly with age, whereas J45 showed no significant relationship. Specifically, J0 decreased by 0.014 D per year of age (95% confidence interval [CI], 0.011–0.016; p < 0.001). Conclusions: The results obtained in this study may provide information to guide surgeons in the management of astigmatism and the choice of toric intraocular lens in cataract surgery. Full article

We investigated the repeatability of the MS-39 in determining power vector components—the spherical equivalent (SEQ) and astigmatic powers (C0 and C45) and asphericity (Q)—of corneal epithelium, stroma, and endothelium in a large patient cohort. In this retrospective cross-sectional single-centre study, we evaluated a dataset containing 600 MS-39 anterior segment tomography measurements from 200 eyes (three repeat measurements each) taken prior to cataract surgery. The exported measurements included height map data for the epithelium, stroma, and endothelium surface. Model surfaces (spherocylinder (SphCyl), cylindrical conoid (CylConoid), and biconic (Biconic), all in the 3/6 mm zone) were fitted using nonlinear iterative optimisation, minimising the height difference between the measurement and model. The mean (MEAN) and standard deviation (SD) for each sequence of measurements were derived and analysed. In the 3 mm and 6 mm zone, the MEAN SEQ was 53.47/53.56/53.57 and 53.21/53.54/53.54 D for SphCyl/CylConoid/Biconic for the epithelium, −4.47/−4.51/−4.51 and −4.45/−4.50/−4.50 D for the stroma, and −6.23/−6.26/−6.26 and −6.18/−6.29/−6.30 D for the endothelium. With the three surface models and the 3/6 mm zone, the SD for SEQ/C0/C45 was in the range of 0.04 to 0.11/0.05 to 0.13/0.04 to 0.11 D for epithelium; 0.01 to 0.02/0.01 to 0.05/0.01 to 0.06 D for stroma; and 0.01 to 0.02/0.02 to 0.07/0.03 to 0.07 D for endothelium. Fitting floating model surfaces with astigmatism to map data of the corneal epithelium, stroma, and endothelium seems to be a robust and reliable method for extracting equivalent power and astigmatism using all the datapoints within a region of interest. Full article

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. Full article

Background/Objectives: Accurate biometric measurements are critical for achieving optimal refractive outcomes in cataract surgery. This study evaluated the agreement of biometric measurements between a swept-source optical coherence tomography (SS–OCT) biometer (Argos^®, Movu Inc.) and an optical low-coherence interferometry (OLCI) biometer (Aladdin^®, Topcon Corp.). Parameters analyzed included axial length (AL), anterior chamber depth (ACD), lens thickness (LT), keratometry (K1, K2), and white-to-white corneal diameter (WTW). Methods: A total of 170 eyes were examined, and agreement was assessed using Bland–Altman analysis, intraclass correlation coefficients (ICCs), and Pearson correlation coefficients. Results: Excellent agreement was observed for AL (ICC = 0.975), ACD (ICC = 0.960), LT (ICC = 0.951), K1 (ICC = 0.921), and K2 (ICC = 0.927). Moderate agreement was found for astigmatism axis (ICC = 0.655) and cylinder power (ICC = 0.891). Poor agreement was noted for astigmatism-related Jackson cross-cylinder vectors J0 (ICC = 0.334) and J45 (ICC = −0.311), as well as for WTW (ICC = 0.338). Bland–Altman plots demonstrated narrow limits of agreement for most parameters, with mean differences of 0.009 mm for AL and 0.06 mm for ACD. Conclusions: Both devices demonstrated high degrees of agreement for core biometric parameters, supporting their clinical interchangeability. However, the variability in WTW and astigmatism-related measurements highlights the need for caution when precise corrections are required. Full article

Purpose: To analyse the correspondence between refractive astigmatism and corneal astigmatism in pseudophakic eyes with non-toric intraocular lenses. Setting: Yeouido St. Mary hospital, Seoul, Republic of Korea. Design: Evaluation of a diagnostic test instrument. Methods: This retrospective study included 95 eyes of 95 patients. Corneal astigmatism was measured with an automated keratometer (RK-5, Canon) and Scheimpflug tomography (Pentacam HR, Oculus). Refractive astigmatism was compared to keratometric astigmatism (based on anterior corneal measurements only), equivalent K-reading, and total corneal astigmatism (both based on anterior and posterior corneal measurements). Vector analysis was carried out by Næser’s polar value method. The accuracy was defined as the average magnitude of the vectorial difference in astigmatism (DA). Each corneal measurement was optimized in retrospect by a multiple linear regression equation between refractive and corneal astigmatism. Results: Keratometric astigmatism overestimated with-the-rule (WTR) refractive astigmatism and underestimated against-the-rule (ATR) refractive astigmatism. Several measurements based on both corneal surfaces’ values did not show any statistically significant difference with respect to refractive astigmatism. The mean corneal astigmatism by total corneal refractive power (TCRP) at 4.0 mm (zone/pupil) produced the lowest mean arithmetic DA and the highest percentage of eyes with a DA ≤ 0.50 dioptre. After optimization, the accuracies of automated KA and TCRP 4.0 mm (zone/pupil) were similar. Conclusions: Total corneal astigmatism measured by Scheimpflug tomography at a 4.0 mm zone centered on the pupil accurately reflects the refractive astigmatism in pseudophakic eyes. However, the accuracy of total corneal astigmatism is not different from automated KA after optimization. Full article

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus. Full article

Background: Posterior keratometry measurements are evolving features of the optical biometers. The differences between devices have bigger impact for the low astigmatism values. The majority of adults present the corneal astigmatism below 1.5 D. Objectives: To compare the total corneal astigmatism measured with two different technologies in cataract patients with corneal astigmatism below 1.5 D. Material and Methods: Three automated exams were performed on each of the two devices: swept-source optical coherence tomography (SS-OCT) and Scheimpflug biometers. The anterior and total corneal astigmatism and power were analysed. Statistical comparisons were performed for within-subject standard deviation, repeatability, Bland–Altman and vector analysis. Results: Twenty-nine eyes of twenty-seven patients were included. The limits of agreement between anterior and total corneal astigmatism were narrower for the SS-OCT than for the Scheimpflug biometer (−0.16 to 0.29 D and −0.40 to 0.39 D, respectively). The >0.5 D difference between SS-OCT and Scheimpflug total astigmatism was noticed in 5 (17%) of cases. The difference between mean total keratometric power for both devices was statistically significant (0.2 D, p < 0.001). SS-OCT total corneal flat measurements had worse repeatability than Scheimpflug (p = 0.007). Conclusions: For the corneal astigmatism <1.5 D, the difference between anterior and total corneal astigmatism measured with SS-OCT was clinically not significant. The mean anterior and total keratometry values obtained with Scheimpflug and SS-OCT biometers are not interchangeable. Full article