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

Early diagnosis of keratoconus using corneal biomechanics and OCT derived technologies

BACKGROUND

Early detection of keratoconus is essential for maximizing the potential of cross-linking treatments designed to halt keratoconus progression, minimizing the risks of iatrogenic ectasia as well as reducing the need for corneal transplantation. This review focuses on the progress that has been made in the early detection of keratoconus using biomechanical and topographical properties derived from three different technologies, namely the ocular response analyser (ORA), corneal visualization Scheimpflug tonometer (Corvis ST) and optical coherence tomography (OCT).

METHOD

A PubMed search was performed using the keywords of 'early keratoconus', 'subclinical keratoconus', 'forme fruste keratoconus', 'very asymmetric ectasia with normal topography/tomography' and 'ocular response analyser' and/or 'Corvis ST'/'corneal visualized Scheimpflug tomographer/tomography' and/or 'optical coherence tomography/tomographer'.

RESULTS

The integration of biomechanical parameters and corneal morphological data from the topography/tomography or OCT, or the assessment of bilateral asymmetry, has demonstrated improvement in the accuracy of diagnosing early-stage keratoconus.

CONCLUSIONS

As measurement principles differ depending on the technique used for keratoconus assessment, comprehensive metrics may be needed to reflect subtle anterior or posterior corneal changes and help identify eyes with very early ectasia. Although clinical experts have always, and will most likely, continue to play a pivotal role in decision-making for early keratoconus diagnosis, future developments in technology and AI may lead to enhanced early detection in the future.

A Novel Approach to Fabricate Early Keratoconus Phantom Models

Purpose

To develop a method to fabricate early keratoconus phantom models and evaluate the feasibility of using corneal models for studying the dynamic response of early keratoconus under an air puff.

Methods

A corneal mold was designed, and the silicone material was poured into the mold to produce corneal phantoms. Two types of early keratoconus phantoms with reduced mechanical properties in a specific area were prepared using a two-step molding process: the central keratoconus phantom and the paracentral keratoconus phantom. Corneal Visualization Scheimpflug Technology tonometry was performed on the normal corneal phantoms and early keratoconus phantoms, and the corresponding dynamic corneal response (DCR) parameters were recorded.

Results

A majority of DCR parameters of the normal corneal phantoms, including deflection amplitude at highest concavity (HCDA), peak distance (PD), radius of curvature (HR), first and second applanation times (A1T and A2T), first and second applanation velocities (A1V and A2V), and the stiffness parameter at the first applanation (SPA1), exhibited trends in response to changes in the simulated intraocular pressure (SIOP) that aligned with experimental results based on ex vivo animal eyes. Significant differences in HCDA, PD, HR, A1V, A2V, A1T, A2T, and integrated radius (IR) were observed between the early keratoconus phantoms and the normal corneal phantoms.

Conclusions

The early keratoconus phantom models fabricated by the present novel approach are feasible for studying the dynamic response of early keratoconus under an air puff.

Translational Relevance

This study demonstrated the potential of corneal phantom models for corneal biomechanical studies, which can deepen our understanding of the DCR parameters, and the results will provide valuable information for early diagnosis of keratoconus.

Using 3D Convolutional Neural Network and Corvis ST Corneal Dynamic Video for Detecting Forme Fruste Keratoconus

PURPOSE

To evaluate the performance of a three-dimensional convolutional neural network (3D CNN) in detecting forme fruste keratoconus (FFKC).

METHODS

A total of 415 anonymized corneal dynamic videos were collected for this study. The video dataset consisted of 150 patients with FFKC (150 videos) and 265 normal patients (265 videos). These patients underwent comprehensive ocular examinations, including slit lamp, Pentacam (Oculus Optikgeräte GmbH), and Corvis ST (Oculus Optikgeräte GmbH), and were classified by corneal experts. A 3D CNN-based algorithm was developed to establish a FFKC detection model. The performance of the model was evaluated using metrics such as accuracy, area under the receiver operating characteristic curve (AUC), confusion matrices, and F1 score. Gradient-weighted class activation mapping (Grad-CAM) was used to observe the regions that the model attended to.

RESULTS

In the test dataset, the model achieved an accuracy of 87.95% in identifying FFKC. The ResNet3D-AUC was 0.95 with a cut-off value of 0.49, and the F1 value was 0.85. The sensitivity was 83.33% and the specificity was 90.57%.

CONCLUSIONS

Combining 3D CNN with Corvis ST corneal dynamic videos provides a new method for distinguishing between FFKC and normal corneas. This could offer valuable clinical insights and recommendations for detecting FFKC. Nevertheless, the generalizability of the model is still a concern, and external validation is required prior to its clinical implementation. [J Refract Surg. 2025;41(4):e356-e364.].

Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection

PURPOSE

To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.

DESIGN

Development and validation of an ML diagnostic algorithm.

METHODS

This retrospective study included 349 eyes of 349 patients with normal, frank keratoconus (KC), and KC suspect (KCS) corneas. KCS corneas included topographically/tomographically normal (TNF) and borderline fellow eyes (TBF) of patients with asymmetric KC. Six parameters were derived from the corneal thickness progression map on the Galilei Dual Scheimpflug-Placido system and fed into a machine-learning algorithm to create the Thickness Speed Progression Index. The model was trained with 5-fold cross-validation using a random search over 7 different ML algorithms, and the best model and hyperparameters were selected.

RESULTS

A total of 133 normal eyes, 141 KC eyes, and 75 KCS eyes, subdivided into 34 TNF and 41 TBF eyes, were included. In experiment 1 (normal and KC), the best model (Random Forest) achieved an accuracy of 100% and area under the receiver operating characteristic (AUROC) of 1.00 for both normal and KC groups. In experiment 2 (normal, KCS, and KC), the model achieved an overall accuracy of 91%, and AUROC curves of 0.93, 0.83, and 0.99 in detecting normal, KCS, and KC corneas respectively. In experiment 3 (normal, TNF, TBF, and KC), the model achieved an accuracy of 87% with AUROC curves of 0.91, 0.60, 0.77, and 0.94 for normal, TNF, TBF, and KC corneas, respectively.

CONCLUSIONS

Using data solely based on pachymetry, ML algorithms such as the Thickness Speed Progression Index are able to discriminate normal corneas from KC and KCSs corneas with reasonable accuracy.

Influence of Dextran Solution and Corneal Collagen Crosslinking on Corneal Biomechanical Parameters Evaluated by Corvis ST In Vitro

Purpose: To analyze the influence of dextran solution and corneal collagen crosslinking (CXL) on corneal biomechanical parameters in vitro, evaluated by Corneal Visualization Scheimpflug Technology (Corvis ST). Materials and Methods: Forty porcine eyes were included in this study. Twenty porcine eyes were instilled with dextran solution for 30 min (10 eyes in 2% dextran solution and 10 eyes in 20% dextran solution). CXL treatment was performed in 10 porcine eyes; the other 10 porcine eyes were regarded as the control group. Each eye was fixed on an experimental inflation platform to carry out Corvis measurements at different IOPs. Corneal biomechanical parameters were calculated based on Corvis measurement. Statistical analysis was used to analyze the influence of dextran solution and CXL on corneal biomechanical parameters based on Corvis parameters. Results: The corneal energy-absorbed area (Aabsorbed) decreased after being instilled with dextran solution under IOP of 15 mmHg (p < 0.001); the elastic modulus (E) of the cornea instilled with 20% dextran solution was significantly higher than that instilled with 2% dextran solution (p < 0.001), since it decreased after being instilled with 20% dextran solution (p = 0.030); the stiffness parameter at the first applanation (SP-A1) increased after CXL (p < 0.001). Conclusions: Both dextran solution and CXL can change corneal biomechanical properties; the concentration of dextran solution can influence the corneal biomechanical properties, which may, in turn, affect the effectiveness of CXL. SP-A1 may be used as an effective parameter for the evaluation of CXL.

Research progress on measurement methods and clinical applications of corneal elastic modulus

Various corneal diseases are strongly associated with corneal biomechanical characteristics, and early measurement of patients' corneal biomechanics can be utilized in their diagnosis and treatment. Measurement methods for corneal biomechanical characteristics are classified into ex vivo and in vivo. Some of these methods can directly measure certain corneal biomechanical parameters, while others require indirect calculation through alternative methods. However, due to diversities in measurement techniques and environmental conditions, significant differences may exist in the corneal mechanical properties measured by these two methods. Therefore, comprehensive research on current measurement methods and the exploration of novel measurement techniques may have great clinical significance. The corneal elastic modulus, a critical indicator in corneal biomechanics, reflects the cornea's ability to return to its initial shape after undergoing stress. This review aims to provide a comprehensive summary of the corneal elastic modulus, which is a critical biomechanical parameter, and discuss its direct, indirect, and potential measurement methods and clinical applications.

Performance of Corvis ST Parameters Including Updated Stress-Strain Index in Differentiating Between Normal, Forme-Fruste, Subclinical, and Clinical Keratoconic Eyes

PURPOSE

This study seeks to evaluate the ability of the updated stress strain index (SSIv2) and other Corvis ST biomechanical parameters in distinguishing between keratoconus at different disease stages and normal eyes.

DESIGN

Diagnostic accuracy analysis to distinguish disease stages.

METHODS

1084 eyes were included and divided into groups of normal (199 eyes), forme fruste keratoconus (FFKC, 194 eyes), subclinical keratoconus (SKC, 113 eyes), mild clinical keratoconus (CKC-Ⅰ, 175 eyes), moderate clinical keratoconus (CKC-Ⅱ, 204 eyes), and severe clinical keratoconus (CKC-Ⅲ, 199 eyes). Each eye was subjected to a Corvis ST examination to determine the central corneal thickness (CCT), biomechanically corrected intraocular pressure (bIOP), SSIv2 (updated stress-strain index), and other 8 Corvis parameters including the stress-strain index (SSIv1), stiffness parameter at first applanation (SP-A1), first applanation time (A1T), Ambrósio relational thickness to the horizontal profile (ARTh), integrated inverse radius (IIR), maximum deformation amplitude (DAM), ratio between deformation amplitude at the apex and at 2 mm nasal and temporal (DARatio2), and Corvis biomechanical index (CBI). The sensitivity and specificity of these parameters in diagnosing keratoconus were analyzed through receiver operating characteristic curves.

RESULTS

Before and after correction for CCT and bIOP, SSIv2 and ARTh were significantly higher and IIR and CBI were significantly lower in the normal group than in the FFKC group, SKC group and the 3 CKC groups (all P < .05). There were also significant correlations between the values of SSIv2, ARTh, IIR, CBI, and the CKC severity (all P < .05). AUC of SSIv2 was significantly higher than all other Corvis parameters in distinguishing normal eyes from FFKC, followed by IIR, ARTh and CBI.

CONCLUSION

Corvis ST's updated stress-strain index, SSIv2, demonstrated superior performance in differentiating between normal and keratoconic corneas, and between corneas with different keratoconus stages. Similar, but less pronounced, performance was demonstrated by the IIR, ARTh and CBI.

Keratoconus Diagnosis and Treatment: Recent Advances and Future Directions

Keratoconus is a disorder characterized by progressive corneal thinning and steepening that may result in significant visual impairment secondary to high astigmatism, corneal scarring, or even corneal perforation. Early detection and screening of keratoconus are essential for effective management and treatment. Several screening methods, such as corneal topography and tomography, corneal biomechanics, and genetic testing, are being developed to detect keratoconus at an early stage. Once detected, prevention of progression is the mainstay of keratoconus management. Corneal collagen cross-linking is a minimally invasive treatment option that can slow or halt the progression of keratoconus. Additionally, recent studies have investigated the potential use of copper sulfate eye drops (IVMED-80) and extracellular vesicles to prevent the progression of keratoconus as non-invasive treatment options. For visual rehabilitation, currently available treatments include scleral lenses, intracorneal ring segments, corneal allogenic intrastromal ring segments, and deep anterior lamellar keratoplasty. The safety and efficacy of these emerging treatment options for keratoconus are currently being investigated.

Detection of Keratoconus With a New Corvis Biomechanical Index Optimized for Chinese Populations

PURPOSE

The aim of this study was to introduce an optimized version of the Corvis Biomechanical Index for Chinese populations (cCBI).

DESIGN

Retrospective, multicenter clinical validity enhancement study.

METHODS

Patients were included from 7 clinics in Beijing, Shenyang, Guangzhou, Shanghai, Wenzhou, Chongqing, and Tianjin, China. Logistic regression was used to optimize the values of the constants of the CBI, based on database 1 as the development dataset (6 of 7 clinics), to create a new version of the index named cCBI. The factors of the CBI (A1Velocity, ARTh, Stiffness Parameter-A, DARatio2mm, and Inverse Integrated Radius) and the cutoff value were kept the same (0.5). With the formation of cCBI determined, it was validated on database 2 (1 of the 7 clinics).

RESULTS

Two thousand four hundred seventy-three patients (healthy and keratoconus) were included. In database 2, the area under the curve of the cCBI was 0.985 with 93.4% specificity and 95.5% sensitivity. In the same dataset, the original CBI produced an area under the curve of 0.978 with 68.1% specificity and 97.7% sensitivity. There was a statistically significant difference between the receiver operating characteristic curve of cCBI and CBI (De Long P = .0009) CONCLUSION: The new cCBI for Chinese patients was shown to be statistically significantly better when compared with CBI to separate healthy from keratoconic eyes. The presence of an external validation dataset confirms this finding and suggests the use of cCBI in everyday clinical practice to aid in the diagnosis of keratoconus in patients who are of Chinese ethnicity.

Quantitative Assessment of Biomechanical Properties of the Human Keratoconus Cornea Using Acoustic Radiation Force Optical Coherence Elastography

Purpose

Quantification of biomechanical properties of keratoconus (KC) corneas has great significance for early diagnosis and treatment of KC, but the corresponding clinical measurement remains challenging. Here, we developed an acoustic radiation force (ARF) optical coherence elastography technique and explored its potential for evaluating biomechanical properties of KC corneas.

Methods

An ARF system was used to induce the tissue deformation, which was detected by an optical coherence tomography system, and thus the localized point-by-point Young's modulus measurements were achieved. Then, two healthy rabbit eyes were imaged to test the system, after which the human keratoconus cornea was evaluated by using the same method. Three regions were selected for biomechanics analysis: the conical region, the transitional region, and the peripheral region.

Results

Young's moduli of transitional region ranged from 53.3 to 58.5 kPa. The corresponding values for the peripheral region were determined to be 58.6 kPa and 63.2 kPa, respectively. Young's moduli of the conical region were gradually increased by 18.3% from the center to the periphery, resulting in the minimum and maximum values of 44.9 kPa and 53.1 kPa, respectively. Furthermore, Young's moduli of the anterior and posterior of the center were determined to be 44.9 kPa and 50.7 kPa, respectively.

Conclusions

Differences in biomechanical properties between the three regions and slight variations within the conical region were clearly distinguished. Biomechanical weakening of the keratoconus cornea was mainly localized in the conical region, especially in the vertex position.

Translational Relevance

The system may provide a promising clinical tool for the noninvasive evaluation of local corneal biomechanics and thus may have potential applications in early keratoconus detection with further optimization.