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

Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning

PURPOSE

This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas.

METHODS

100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters.

RESULTS

The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation.

CONCLUSION

This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus.

CorNet: Autonomous feature learning in raw Corvis ST data for keratoconus diagnosis via residual CNN approach

PURPOSE

To ascertain whether the integration of raw Corvis ST data with an end-to-end CNN can enhance the diagnosis of keratoconus (KC).

METHOD

The Corvis ST is a non-contact device for in vivo measurement of corneal biomechanics. The CorNet was trained and validated on a dataset consisting of 1786 Corvis ST raw data from 1112 normal eyes and 674 KC eyes. Each raw data consists of the anterior and posterior corneal surface elevation during air-puff induced dynamic deformation. The architecture of CorNet utilizes four ResNet-inspired convolutional structures that employ 1 × 1 convolution in identity mapping. Gradient-weighted Class Activation Mapping (Grad-CAM) was adopted to visualize the attention allocation to diagnostic areas. Discriminative performance was assessed using metrics including the AUC of ROC curve, sensitivity, specificity, precision, accuracy, and F1 score.

RESULTS

CorNet demonstrated outstanding performance in distinguishing KC from normal eyes, achieving an AUC of 0.971 (sensitivity: 92.49%, specificity: 91.54%) in the validation set, outperforming the best existing Corvis ST parameters, namely the Corvis Biomechanical Index (CBI) with an AUC of 0.947, and its updated version for Chinese populations (cCBI) with an AUC of 0.963. Though the ROC curve analysis showed no significant difference between CorNet and cCBI (p = 0.295), it indicated a notable difference between CorNet and CBI (p = 0.011). The Grad-CAM visualizations highlighted the significance of corneal deformation data during the loading phase rather than the unloading phase for KC diagnosis.

CONCLUSION

This study proposed an end-to-end CNN approach utilizing raw biomechanical data by Corvis ST for KC detection, showing effectiveness comparable to or surpassing existing parameters provided by Corvis ST. The CorNet, autonomously learning comprehensive temporal and spatial features, demonstrated a promising performance for advancing KC diagnosis in ophthalmology.

Evaluation of changes in corneal biomechanics after orthokeratology using Corvis ST

PURPOSE

To investigate the alterations in corneal biomechanical metrics induced by orthokeratology (ortho-k) using Corvis ST and to determine the factors influencing these changes.

METHOD

A prospective observational study was conducted to analyze various Corvis ST parameters in 32 children with low to moderate myopia who successfully underwent ortho-k lens fitting. Corneal biomechanical measurements via Corvis ST were acquired at six distinct time points: baseline (pre) and 2 h (pos2h), 6 h (pos6h), and 10 h (pos10h) following the removal of the first overnight wear ortho-k, one week (pos1w) and one month (pos1m) subsequent to the initiation of ortho-k.

RESULT

Significant differences were observed in Corvis ST Biomechanical parameters DAR2, IIR, CBI, and cCBI post ortho-k intervention. The integration of covariates (CCT, SimK, and bIOP) mitigated the differences in DAR2, IIR, and cCBI, but not in CBI. Initially, the stiffness parameter at first applanation, SP-A1, did not demonstrate significant variations, but after adjusting for covariates, noticeable differences over time were observed. The Stress-Strain Indeces, SSIv1 and SSIv2, did not manifest considerable changes over time, irrespective of the adjustment for covariates. No significant disparities were identified among different ortho-k lens brands.

CONCLUSION

Corneal biomechanics remained consistent throughout the one-month period of ortho-k lens wear. The observed changes in Corvis ST parameters subsequent ortho-k are primarily attributable to alterations in corneal pachymetry and morphology, rather than actual alterations in corneal biomechanics. The stability of corneal biomechanics post ortho-k treatment suggests the safety of this approach for adolescents from a corneal biomechanics perspective.

The extraction and application of antisymmetric characteristics of the cornea during air-puff perturbations

BACKGROUND

A non-contact tonometer is used to measure intraocular pressure, and studies have primarily relied on apex displacements to assess corneal properties. However, previous studies have overlooked the asymmetric characteristics of lateral corneal perturbations, leading to a gap in understanding of the lateral mechanical properties and its application.

METHOD

To investigate these lateral perturbations, we designed an experiment to sequentially record the corneal profiles when two consecutive air-puffs were applied at the center of the same cornea within a short period. Moreover, we used modal decomposition to decompose anterior surface profiles into symmetric and antisymmetric modes to comprehensively analyze the asymmetric characteristics. To extract mechanical properties, we utilized high-pass frequency analysis (>250 Hz) to filter out noise and errors.

RESULTS

Symmetric modes between the two consecutive air-puffs exhibited major similarities during vibration; however, antisymmetric modes exhibited minor differences in lateral perturbations of asymmetric vibration. The antisymmetric modes might be related to air-puff misalignment and mechanical properties. Through applying frequency analysis, the mechanical properties could be proven at high frequencies and misalignment shown at low frequencies. Furthermore, we compared the corneal vibration profiles of 259 healthy participants and 50 patients with keratoconus. Their properties showed that the antisymmetric modes of the keratoconus group exhibited a completely opposite direction of deformation compared to that in the healthy group.

CONCLUSIONS

Our proposed algorithm not only extracts antisymmetric characteristics but also offers valuable insights into decompose misalignment and mechanical properties of healthy and keratoconus corneas, presenting a new perspective for corneal biomechanics.

Assessment of the specificity of corvis biomechanical index-laser vision correction (CBI-LVC) in stable corneas after phototherapeutic keratectomy

PURPOSE

The Corvis Biomechanical Index-Laser Vision Correction (CBI-LVC) is a biomechanical index to detect ectasia in post-refractive surgery patients (PRK, LASIK, SMILE). This study aims to evaluate the distribution of the CBI-LVC in stable patients who underwent Phototherapeutic Keratectomy (PTK) compared to PRK patients.

METHODS

Patients who underwent PRK and PTK performed between 2000 and 2018 in Humanitas Research Hospital, Rozzano, Milan, Italy and remained stable for at least four years post-surgery were included. All eyes were examined with the Corvis ST (Oculus, Germany), whose output allows the calculation of the CBI-LVC. The distribution and specificity of the CBI-LVC in the two populations were estimated using a Wilcoxon Mann-Whitney test and compared.

RESULTS

175 eyes of 148 patients were included (85 eyes of 50 PTK patients and 90 eyes of 90 PRK patients). The distribution of CBI-LVC in the two groups showed a minor difference, with a median value in PRK patients of 0.000 (95% CI 0.000; 0.002) and 0.008 (95% CI 0.000; 0.037) in PTK patients (Mann-Whitney U test p = 0.023). The statistical analysis showed that the CBI-LVC provided a specificity of 92.22% in the PRK group, while in the PTK group it was 82.35%. Nevertheless, this difference was not statistically significant (Chi-squared test with Yates, p = 0.080).

CONCLUSION

CBI-LVC provided similar specificity in stable PTK patients compared to those who underwent PRK. These results suggest that the CBI-LVC could be a useful tool to aid corneal surgeons in managing PTK patients.

Management of keratoconus: an updated review

Keratoconus is the most common corneal ectatic disorder. It is characterized by progressive corneal thinning with resultant irregular astigmatism and myopia. Its prevalence has been estimated at 1:375 to 1:2,000 people globally, with a considerably higher rate in the younger populations. Over the past two decades, there was a paradigm shift in the management of keratoconus. The treatment has expanded significantly from conservative management (e.g., spectacles and contact lenses wear) and penetrating keratoplasty to many other therapeutic and refractive modalities, including corneal cross-linking (with various protocols/techniques), combined CXL-keratorefractive surgeries, intracorneal ring segments, anterior lamellar keratoplasty, and more recently, Bowman's layer transplantation, stromal keratophakia, and stromal regeneration. Several recent large genome-wide association studies (GWAS) have identified important genetic mutations relevant to keratoconus, facilitating the development of potential gene therapy targeting keratoconus and halting the disease progression. In addition, attempts have been made to leverage the power of artificial intelligence-assisted algorithms in enabling earlier detection and progression prediction in keratoconus. In this review, we provide a comprehensive overview of the current and emerging treatment of keratoconus and propose a treatment algorithm for systematically guiding the management of this common clinical entity.