Aberration Correction with Aspheric Intraocular Lenses

The shape of the normal human cornea induces positive spherical aberration (SA) which causes image blur. In the young phakic eye, the crystalline lens compensates for a certain amount of this corneal aberration. However, the compensation slowly decreases with the aging lens and is fully lost after cataract extraction and implantation of a standard intraocular lens (IOL). Conventional spherical IOLs add their intrinsic positive SA to the positive SA of the cornea increasing the image blur. As a useful side effect, this also increases the depth of focus—often referred to as pseudo-accommodation. Aspheric intraocular lenses have been introduced to be either neutral to SA or to compensate for a certain amount of corneal SA. A customized correction for the individual eye seems to be the most promising solution for tailored correction of SA. In this chapter we will provide detailed information on the various concepts of aspheric intraocular lenses to elucidate that the term “aspheric intraocular lens” is being used for a large amount of different lens designs

Imaging the Cornea, Anterior Chamber, and Lens in Corneal and Refractive Surgery

Anterior segment OCT (AS-OCT) is an optical and noncontact imaging technology, which has numerous fields of application in the imaging of the cornea, anterior chamber, and the lens. In this chapter, we will present some of the application fields of AS-OCT in corneal, cataract, and refractive surgery. We will emphasize the potential of AS-OCT by several clinical examples including corneal imaging (keratoconus, keratoplasty, and refractive surgery) and intraocular lens imaging after refractive surgery. AS-OCT shows special potential for corneal imaging in case of corneal edema and for postoperative control after Descemet’s membrane endothelial keratoplasty (DMEK). The postoperative follow-up of a posterior chamber Collamer lens’ses vault and measuring the anterior chamber angle could be identified as another promising field of application for AS-OCT

Complementary Keratoconus Indices Based on Topographical Interpretation of Biomechanical Waveform Parameters: A Supplement to Established Keratoconus Indices

Purpose:To build new models with the Ocular Response Analyzer (ORA) waveform parameters to create new indices analogous to established topographic keratoconus indices.Method:Biomechanical, tomographic, and topographic measurements of 505 eyes from the Homburger Keratoconus Centre were included. Thirty seven waveform parameters (WF) were derived from the biomechanical measurement with the ORA. Area under curve (ROC, receiver operating characteristic) was used to quantify the screening performance. A logistic regression analysis was used to create two new keratoconus prediction models based on these waveform parameters to resample the clinically established keratoconus indices from Pentacam and TMS-5.Results:ROC curves show the best results for the waveform parameters P1area, P2area, h1, h2, dive1, mslew1, aspect1, aplhf, dslope1. The new keratoconus prediction model to resample the Pentacam topographic keratoconus index (TKC) was: WFTKC = –4.068 + 0.002×P2area – 0.005×dive1 – 0.01×h1 – 2.501×aplhf, which achieves a sensitivity of 90.3% and specificity of 89.4%; to resample the TMS-5 keratoconus classification index (KCI) it was: WFKCI = –3.606 + 0.002×P2area which achieves a sensitivity of 75.4% and a specificity of 81.8%.Conclusion:Additional to the biomechanically provided Keratoconus Index two new indices which were based on the topographic gold standards (either Pentacam or TMS-5) were created. Of course, these do not replace the original topographic measurement

Is the Memory Effect of the Blind Spot Involved in Negative Dysphotopsia after Cataract Surgery?

We present novel clinical observations on negative dysphotopsia (ND) in eyes that have undergone cataract surgery. In the past, shadow effects were alleged to be located in the far peripheral temporal visual field 50° to 100° away from the optical axis. In a small series of eight patients we found evidence of photic effects, described by the patients as shadows in the periphery that were objectively located much more centrally. In all cases, we could find an association of these phenomena with the blind spot. We hypothesize that the memory effect of the blind spot which is dislocated and changed in magnification due to replacement of the crystalline lens could be one determinant for pseudophakic ND. The scotoma of the optic nerve head and the main arteries and veins of the phakic eye are displaced in the pseudophakic eye depending on the specific characteristics and position of the intraocular lens within the eye

Hypothyroidism is Not Associated with Keratoconus Disease: analysis of 626 Subjects

L

IOL Formula Constants: Strategies for Optimization and Defining Standards for Presenting Data

Purpose: The aim of this study is to present strategies for optimization of lens power (IOLP) formula constants and to show options how to present the results adequately. Methods: A dataset of N = 1,601 preoperative biometric values, IOLP data and postoperative refraction data was split into a training set and a test set using a random sequence. Based on the training set, we calculated the formula constants for established lens calculation formulae with different methods. Based on the test set, we derived the formula prediction error (PE) as difference of the achieved refraction from the formula predicted refraction. Results: For formulae with 1 constant, it is possible to back-calculate the individual constant for each case using formula inversion. However, this is not possible for formulae with >1 constant. In these cases, more advanced concepts such as non-linear optimization strategies are necessary to derive the formula constants. During cross-validation, measures such as the mean absolute or the root mean squared PE or the ratio of cases within mean absolute PE (MAE) limits could be used as quality measures. Conclusions: Different constant optimization concepts yield different results. To test the performance of optimized formula constants, a cross-validation strategy is mandatory. We recommend performance curves, where the ratio of cases within absolute PE limits is plotted against the MAE

Simulation of Corneal imaging properties for near objects

Purpose Using raytracing simulation to study the effect of corneal imaging metrics for different aperture sizes as a function of object distances with different schematic model eyes. Methods This raytracing simulation determined the best focus (with the least root‐mean‐square (rms) ray scatter) and the best wavefront focus (with least rms wavefront error) for four schematic model eyes (Liou‐Brennan (LBME), Atchison (ATCHME), Gullstrand (GULLME) and Navarro (NAVME)) with 4 aperture sizes (2–5 mm) and 30 object distances in a logscale from 10 cm to 10 m plus infinity. For each configuration, 10,000 rays were traced through the cornea, and the aperture stop was located at the lens front apex plane as described in the model eyes. The wavefront was decomposed into Zernike components to extract the spherical aberration term. Results The focal distance with respect to the corneal front apex increases from around 31 mm for objects at infinity to around 40 mm for objects at 10 cm. The best (wavefront) focus was systematically closer to the cornea compared with the paraxial focus, and the overestimation of focal length with the paraxial focus was larger for large aperture sizes and small object distances. The rms ray scatter and wavefront error were both systematically larger with large aperture and small object sizes. At best focus the rms wavefront error was systematically larger, and the rms ray scatter was systematically smaller compared to the best wavefront focus. Spherical aberration varied more with GULLME than with LBME or NAVME, and increased strongly at smaller object distances. Conclusions The imaging properties of the cornea, especially spherical aberration, increase strongly as the object distance decreases. This effect should be considered, especially when considering aberration correcting lenses for near vision such as multifocal or enhanced depth of focus lenses

Ratio of torus and equivalent power to refractive cylinder and spherical equivalent in phakic lenses – a Monte‐Carlo simulation study

Background: Spherical and astigmatic powers for phakic intraocular lenses are frequently calculated using fixed ratios of phakic lens refractive power to refractive spherical equivalent, and of phakic lens astigmatism to refractive cylinder. In this study, a Monte-Carlo simulation based on biometric data was used to investigate how variations in biometrics affect these ratios, in order to improve the calculation of implantable lens parameters. Methods: A data set of over sixteen thousand biometric measurements including axial length, phakic anterior chamber depth, and corneal equivalent and astigmatic power was used to construct a multidimensional probability density distribution. From this, we determined the axial position of the implanted lens and estimated the refractive spherical equivalent and refractive cylinder. A generic data model resampled the density distributions and interactions between variables, and the implantable lens power was determined using vergence propagation. Results: 50 000 artificial data sets were used to calculate the phakic lens spherical equivalent and astigmatism required for emmetropization, and to determine the corresponding ratios for these two values. The spherical ratio ranged from 1.0640 to 1.3723 and the astigmatic ratio from 1.0501 to 1.4340. Both ratios are unaffected by the corneal spherical / astigmatic powers, or the refractive cylinder, but show strong correlation with the refractive spherical equivalent, mild correlation with the lens axial position, and moderate negative correlation with axial length. As a simplification, these ratios could be modelled using a bi-variable linear regression based on the first two of these factors. Conclusion: Fixed spherical and astigmatic ratios should not be used when selecting high refractive power phakic IOLs as their variation can result in refractive errors of up to ±0.3 D for a 8 D lens. Both ratios can be estimated with clinically acceptable precision using a linear regression based on the refractive spherical equivalent and the axial position

Corneal back surface power – interpreting keratometer readings and what predictions can tell us

The classical Javal's rule allows estimation of refractive cylinder from keratometric astigmatism using scaling for vergence transformation, with an additional half dioptre of cylinder against-the-rule. With increasing popularity of toric intraocular lenses it has been shown that keratometric astigmatism does not fully reflect the entire astigmatism of the phakic or pseudophakic eye. Researchers mostly argue that this mismatch is primarily due to astigmatism of the corneal back surface, and some papers propose correction strategies to consider this mismatch with the keratometric values. In this Technical Note we address this issue using a vector analysis and show the consequences of this correction on the front and back surface as well as total astigmatism of the cornea. As examples we focus on the correction strategies proposed by Abulafia and by Savini, frequently used in clinical practice. The main conclusion is that, since corneal tomographers do not systematically show zero total astigmatism in situations where keratometry measures astigmatism against-the-rule of around 3 dioptres, there may be reasons other than the corneal back surface for this mismatch between keratometry and total astigmatism. A number of possible sources of this mismatch are proposed

Staging of keratoconus indices regarding tomography, topography, and biomechanical measurements

Purpose To derive limits of metric keratoconus indices for classification into keratoconus stages. Design Validity and reliability analysis of diagnostic tools. Methods A total of 126 patients from the keratoconus center of Homburg/Saar were evaluated with respect to Amsler criteria, using Pentacam (Keratoconus Index [KI], Topographic Keratoconus Classification [TKC]), Topographic Modeling System (Smolek/Klyce, Klyce/Maeda), and Ocular Response Analyzer (Keratoconus Match Probability [KMP], Keratoconus Match Index [KMI]). Mean value, standard deviation, 90% confidence interval, and the Youden J index for definition of the thresholds were evaluated. Results For separation of keratoconus stages 0/1/2/3/4 we derived the following optimum thresholds: for KI 1.05/1.15/1.31/1.49 and for KMI 0.77/0.32/-0.08/-0.3. For Smolek/Klyce and Klyce/Maeda high standard deviations and overlapping confidence intervals were found; therefore no discrete thresholds could be defined. Nevertheless, for them we still found a good sensitivity and specificity in discriminating between healthy (stage 0) and keratoconus (stages 2–4) eyes in comparison with the other indices. Conclusions We derived thresholds for the metric keratoconus indices KI and KMI, which allow classification of keratoconus stages. These now need to be validated in clinical use. Smolek/Klyce and Klyce/Maeda were not sufficiently sensitive to allow classification into individual stages, but these indices did show a good specificity and sensitivity in discriminating between keratoconus and healthy eyes