Comparative evaluation of methods for obtaining the stress-free geometry of keratoconus corneas with different levels of severity

A biomechanical model that simulates the physiological pressure load on the cornea without considering the stresses in vivo will result in an overstress or underestimation of the stress field and incorrect deformation of the corneal structure. Therefore, it is essential to propose numerical models that consider the stress-free geometry of the cornea. In this study, the Displacement and Pre-stress methods were compared to obtain the stress-free geometry (S-FG) and the physiological estimated geometry (P-EG), based on the patient-specific geometric behavior and the computational time required to reach each geometry. The same shape and contour conditions were considered in the models obtained from both methods for each of the pathological scenarios analyzed. Both methods behaved differently to obtain the free geometry, and this difference increased with the severity grade of the disease. However, they behaved in a similar way to reach the physiological estimated geometry. The Displacement method required a lower computational cost to reach the free geometry, with both methods presenting a similar computational cost to obtain the physiological geometry. The stress-free geometries obtained by both methods allowed to characterize the existing biomechanical decompensation during the progression of the diseases. In conclusion, the calculation of the stress-free corneal geometry associated to the clinically measured intraocular pressure with the Displacement and Prestress Methods in keratoconus eyes allows the development of accurate and useable models in clinical practice in real time. This displacement method shows some benefits in terms of computational cost.

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.

Spotlight on the Corneal Back Surface Astigmatism: A Review

Recent evidence indicates that the corneal back surface astigmatism (CBSA) contributes to the refractive state of the eye in cataract surgery, especially with the implantation of toric intraocular lenses. But this has been met with some scepticism. A review of key studies performed over the past three decades shows that the mean CBSA power ranges from 0.18(±0.16)D to 1.04(±0.20)D. The clinical assessment of CBSA is problematic. There is poor agreement between the current automated systems for assessment of CBSA and it is assumed that these systems directly measure the CBSA. But CBSA cannot be measured directly in vivo. A historical review of methods used to quantify the curvature of the posterior corneal surface reveals that CBSA estimated by current systems is based on values for corneal front surface astigmatism, corneal refractive index, central corneal thickness, corneal thickness at peripheral locations and the exact distance between the corneal apex and each one of these peripheral locations. Doubts and errors in these values, coupled with the precise details of the algorithm incorporated to estimate CBSA, are the likely sources of the lack of agreement between current systems. These systematic errors cloud the assessment of CBSA. Mean CBSA may be low, but it varies from case to case. There is a clear need for a realistic, practical procedure for clinicians to independently calibrate systems for estimating CBSA. This would help to reduce uncertainty and the discrepancies between instruments designed to measure the same parameter.

The influence of routine uncomplicated phacoemulsification on the orthogonality of the cornea

Purpose:

The aim of this study was to determine the effect of routine uncomplicated phacoemulsification on the orthogonal distribution of mass within the central optical zone of the cornea.

Methods:

Astigmatism at both corneal surfaces was evaluated using Orbscan II (Bausch &and Lomb) before and up to 3 months after routine phacoemulsification (one eye/patient). The data were subjected to vector analysis to estimate the pre-and postoperative total astigmatism of the cornea (TCA).

Results:

Reporting the chief findings in minus cylinder (diopters, DC) over the central 3 mm (A) and 5 mm (B) optical zones. Mean TCA powers (±sd) at pre- and 3-months postop were A) –4.45DC (±2.00) and –5.69DC (±2.69), B) –2.91DC (±2.22) and –2.71DC (±1.60). Change in mean power was significant over 3 mm (P < 0.01, n = 49) but not over 5 mm. Inter-zonal differences were significant (P < 0.01). There was a significant linear relationship between the change in TCA power (y = preoperative-postoperative) and TCA at preoperative stage (x) where, A) y = 0.45x + 3.12 (r = 0.336, n = 49, P = 0.018), B) y = x + 2.65 (r = 0.753, n = 49, P = <0.01). Over the central 3 mm zone only, change (preoperative-postoperative) in axis (°) of TCA (y₁) was significantly associated with TCA axis at preoperative stage (x₁) where y₁ = 1.391x₁-0.008x₁²-0.701 (r = 0.635, n = 49, P < 0.01).

Conclusion:

Changes in TCA power and axis at 3 months postop, determined using Orbscan II, are indicative of orthogonal alterations in the distribution of corneal tissue. Over the central 3 mm zone, the association between y₁ and x₁ shows that a change in TCA axis is more profound when preoperative axis is near 90° i.e., against-the-rule.

Biometric Measurement of Anterior Segment: A Review

Biometric measurement of the anterior segment is of great importance for the ophthalmology, human eye modeling, contact lens fitting, intraocular lens design, etc. This paper serves as a comprehensive review on the historical development and basic principles of the technologies for measuring the geometric profiles of the anterior segment. Both the advantages and drawbacks of the current technologies are illustrated. For in vivo measurement of the anterior segment, there are two main challenges that need to be addressed to achieve high speed, fine resolution, and large range imaging. One is the motion artefacts caused by the inevitable and random human eye movement. The other is the serious multiple scattering effects in intraocular turbid media. The future research perspectives are also outlined in this paper.

The Impact of Changes in Corneal Back Surface Astigmatism on the Residual Astigmatic Refractive Error following Routine Uncomplicated Phacoemulsification

Purpose

To determine the significance of any association between intersessional changes in ocular residual astigmatism (RA) and astigmatism at corneal front (FSA) and back (BSA) surfaces following uneventful routine phacoemulsification.

Methods

Astigmatism was evaluated by autorefractometry and subjective refraction and at both the corneal surfaces with Orbscan II™ (Bausch & Lomb) over central 3 mm and 5 mm optical zones at 1, 2, and 3 months after routine phacoemulsification in 103 patients implanted with monofocal nontoric intraocular lenses (IOLs, one eye/patient). Data were subjected to vector analysis to determine the actual change (Δ) in astigmatism (power and axis) for the refractive and Orbscan II findings.

Results

The number of cases that attended where ΔRA was ≥0.50 DC between 1 and 2 months was 52 by autorefractometry and 36 by subjective refraction and between 2 and 3 months was 24 by autorefractometry and 19 by subjective refraction. Vector analysis revealed significant correlations between ΔFSA and ΔRA for data obtained by autorefractometry but not by subjective refraction. At all times, ΔBSA was greater than ΔFSA (p < 0.01). Key findings for ΔBSA values over the central 3 mm zone were between (A) the sine of the axis of ΔRA (y) and sine of the axis of ΔBSA (x) for the data obtained by autorefractometry (between 1 and 2 months, y = 0.749 - 0.303x, r = 0.299, n = 52, p=0.031) and subjective refraction (between 2 and 3 months, y = 0.6614 - 0.4755x, r = 0.474, n = 19, p=0.040) and (B) ΔRA (y) and ΔBSA (x) powers between 2 and 3 months postoperatively for the data obtained by autorefractometry (ΔRA = 0.118 ΔBSA + 0.681 r = 0.467, n = 24, p=0.021) and subjective refraction (ΔRA = 0.072 ΔBSA + 0.545 r = 0.510, n = 19, p=0.026).

Conclusion

Changes in the ocular residual refractive astigmatic error after implanting a monofocal nontoric IOL are associated with changes in astigmatism at the back surface of the cornea within the central optical zone.

Rotating Scheimpflug Imaging Indices in Different Grades of Keratoconus

Purpose. To evaluate accuracy of various Keratoconus (KC) screening indices, in relation to Topographic Keratoconus (TKC) grading. Setting. Al Watany Eye Hospital, Cairo, Egypt. Methods. Data of 103 normal (group 1) and 73 KC eyes (group 2), imaged by Pentacam (branded as Allegro Oculyzer), were analysed. Group 2 was divided into 2a: 14 eyes (TKC = 1, early KC), 2b: 25 eyes (TKC = 1 to 2 or 2, moderate KC), and 2c: 34 eyes (TKC = 2 to 3 up to 4, severe KC). Participants were followed up for six years to confirm diagnosis. Area under the receiver operating characteristic curve (AUROC) was calculated for evaluated curvature, elevation, and pachymetry indices with various reference shapes at different diameters. Results. When comparing normal to KC eyes, ten indices had significantly higher AUROC. Only five of them had significantly higher AUROC in early KC compared to normal corneas: Pachymetry Progression Index- (PPI-) Maximum (Max), Ambrósio's Relational Thickness- (ART-) Max, PPI-Max minus PPI-Minimum (Min), central corneal thickness (CCT), and diagonal decentration of thinnest point from the apex (AUROC = 0.690, 0.690, 0.687, 0.683, and 0.674, resp.). Conclusion. Generally, ten pachymetry and elevation-based indices had significantly higher AUROC. Five indices had statistically significant high AUROC when comparing early KC to normal corneas.