Biomechanical properties of early keratoconus: Suppressed deformation signal wave

Fourteen years follow-up of a stable unilateral Keratoconus: unique case report of clinical, tomographical and biomechanical stability

Background

Keratoconus (KC) is a noninflammatory corneal ectatic disorder. In 2015, the Global Consensus on Keratoconus and Ectatic Diseases agreed that the pathophysiology of KC includes environmental, biomechanical, genetic, and biochemical disorders on one hand, and that true unilateral KC does not exist on the other hand. However, with the increasingly advancements in detection methods, we report the first case of a stable unilateral keratoconus with the longest follow up period of 14 years (2006–2020). We used topographic, tomographic, and biomechanical values for both eyes over the years to confirm the diagnosis, which has never been done before. Our study focuses on a single patient therefore it illustrates the mere possibility that unilateral keratoconus exists.

Case presentation

We present the case of a 19-year-old male with no previous ocular or general health conditions who presented to our clinic in November 2006 for incidental finding of decreased vision of the right eye (OD) on a routine examination. Topographies, tomographies, and biomechanical analysis of both eyes were obtained and showed a unilateral right keratoconus at the time. Patient admitted to unilateral right eye rubbing. Although we cannot prove that previous eye rubbing alone led to these initial symptoms, he was advised to stop rubbing and was followed up without any intervention for fourteen years during which topographic, tomographic, and biomechanical values for both eyes remained stable, proving for the first time that unilateral KC could exist.

Conclusion

We think that the data we are presenting is important because acknowledging that true unilateral keratoconus exists questions the genetic or primary biomechanical etiology of keratoconus versus the secondary biomechanical etiologies like eye rubbing. Our report also shows the importance of corneal biomechanics in detecting early changes. This is important to detect early, prevent progression, and tailor treatment.

A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients

Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC).

Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were without morphological abnormality, while the contralateral eye was diagnosed as clinical keratoconus), and 50 healthy patients (50 eyes) were enrolled and underwent Corvis measurements. We calculated E according to the relation between airpuff force and corneal apical displacement. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were used to identify the predictive accuracy of the E and other dynamic corneal response (DCR) parameters. Besides, we used backpropagation (BP) neural network to establish the keratoconus diagnosis model.

Results: 1) There was significant difference between KC and healthy subjects in the following DCR parameters: the first/second applanation time (A1T/A2T), velocity at first/second applanation (A1V/A2V), the highest concavity time (HCT), peak distance (PD), deformation amplitude (DA), Ambrosio relational thickness to the horizontal profile (ARTh). 2) A1T and E were smaller in FFKC and KC compared with healthy subjects. 3) ROC analysis showed that E (AUC = 0.746) was more accurate than other DCR parameters in detecting FFKC (AUC of these DCR parameters was not more than 0.719). 4) Keratoconus diagnosis model by BP neural network showed a more accurate diagnostic efficiency of 92.5%. The ROC analysis showed that the predicted value (AUC = 0.877) of BP neural network model was more sensitive in the detection FFKC than the Corvis built-in parameters CBI (AUC = 0.610, p = 0.041) and TBI (AUC = 0.659, p = 0.034).

Conclusion: Corneal elastic modulus was found to have improved predictability in detecting FFKC patients from healthy subjects and may be used as an additional parameter for the diagnosis of keratoconus.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

BACKGROUND

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.

OBJECTIVE

The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.

METHODS

For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.

RESULTS

We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.

CONCLUSIONS

Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Relationship between Corneal Morphogeometrical Properties and Biomechanical Parameters Derived from Dynamic Bidirectional Air Applanation Measurement Procedure in Keratoconus

The morphogeometric analysis of the corneal structure has become a clinically relevant diagnostic procedure in keratoconus (KC) as well as the in vivo evaluation of the corneal biomechanical properties. However, the relationship between these two types of metrics is still not well understood. The current study investigated the relationship of corneal morphogeometry and volume with two biomechanical parameters: corneal hysteresis (CH) and corneal resistance factor (CRF), both provided by an Ocular Response Analyzer (Reichert). It included 109 eyes from 109 patients (aged between 18 and 69 years) with a diagnosis of keratoconus (KC) who underwent a complete eye examination including a comprehensive corneal topographic analysis with the Sirius system (CSO). With the topographic information obtained, a morphogeometric and volumetric analysis was performed, defining different variables of clinical use. CH and CRF were found to be correlated with these variables, but this correlation was highly influenced by corneal thickness. This suggests that the mechanical properties of KC cornea contribute only in a partial and limited manner to these biomechanical parameters, being mostly influenced by morphogeometry under normal intraocular pressure levels. This would explain the limitation of CH and CRF as diagnostic tools for the detection of incipient cases of KC.

Keratoconus Screening Using Values Derived From Auto-Keratometer Measurements: A Multicenter Study

PURPOSE

Screening of early-stage keratoconus using auto-keratometer parameters.

DESIGN

Evaluation of a screening approach.

METHODS

At 5 major centers in Japan, we enrolled 123 eyes of 123 patients with Amsler-Krumeich classification stage 1 (<50 years of age [average 26.36 ± 8.68 years]; 84/39 male/female) and 205 eyes of 205 healthy subjects (average age 26.20 ± 7.34 years, 139/66 male/female). Participants were divided 2:1 into a prediction group and an application group. In the prediction group, multivariate logistic regression analysis was performed with keratoconus diagnosis as the dependent variable, and auto-keratometer parameters including average K, steep K, flat K, astigmatism, and astigmatic axis (no, with-the-rule, against-the-rule, and oblique) as independent variables. The diagnostic probability determined by regression analysis was defined as the keratometer keratoconus index. The cutoff value was determined from the receiver operating characteristic curve. This prediction equation was evaluated in the application group. Our primary outcome measure was the accuracy of the prediction equation for discriminating keratoconus from normal eyes.

RESULTS

The selected explanatory variables were steep K (partial regression coefficient [β] 1.284, odds ratio [OR] 3.610), flat K (β -0.618, OR 0.539), and with-the-rule astigmatism (β -3.163, OR 0.042). The area under the receiver operating characteristic curve of keratometer keratoconus index was 0.90, which was significantly better than individual parameters (P < .001). The sensitivity and specificity values in the application group were 85.0% and 86.7%, respectively.

CONCLUSIONS

Although the sensitivity/specificity was not high, the new prediction equation using auto-keratometer-derived parameters enabled better discrimination of early-stage keratoconus than the isolated parameters.

Visual acuity in patients with keratoconus: a comparison with matched regular myopic astigmatism

PURPOSE

To compare uncorrected distance visual acuity (UDVA) and best-corrected distance visual acuity (CDVA) between patients with keratoconus (KC) and matched patients with regular myopic astigmatism.

METHODS

This retrospective study included consecutive patients diagnosed with KC between 2008 and 2013 at Care-Vision Laser Centers, Tel-Aviv, Israel, and matched patients with regular myopic astigmatism. Data included were central corneal thickness (CCT), spherical equivalent (SE), cylinder (CYL), mean keratometric power, maximum keratometric power (K_max), UDVA, CDVA, and defocus equivalent (DEQ).

RESULTS

The KC group included 734 patients with a mean age of 33.8 ± 9.5 years. The matched, control group included 1462 patients with a mean age of 33.2 ± 9.7 years (p = 0.14). The mean SE and CYL of the KC group were - 3.34 ± 3.29D and - 3.01 ± 1.99D, respectively, compared to - 3.34 ± 2.92D (p = 0.98) and - 2.97 ± 1.35 (p = 0.58). Mean K (46.8 ± 3.3D versus 44.0 ± 1.8D, p < 0.0001) and K_max (48.4 ± 4.0D versus 45.3 ± 2.0D, p < 0.0001) were statically significant higher in the KC group. CCT was significantly thinner in the KC group (444 ± 49 versus 527 ± 40 μm, p < 0.0001). The KC group had a better UDVA than the non-KC group (1.10 ± 0.68 versus 1.22 ± 0.64 logMAR, p < 0.0001). CDVA was significantly lower in the KC group (p < 0.001).

CONCLUSIONS

For defocus equivalents above 6D, the KC group had better UDVA than the non-KC group in spite of worse CDVA.