Application of corneal tomography before keratorefractive procedure for laser vision correction

Abstract

Ectasia after refractive surgery represents a major concern among refractive surgeons. Corneal abnormalities and preexisting corneal ectasia are the most important risk factors. Corneal topography and central corneal thickness are the factors traditionally screening for in refractive surgery candidates. Study of the anterior surface by Placido topography allows for identification of keratoconus before biomicroscopy. However, this is insufficient for the evaluation of pre-operative refractive surgery. There are cases of ectasia after laser in situ keratomilusis (LASIK) without identifiable risk factors such that there is a need to go beyond the corneal surface. A key requirement is quantifying susceptibility to corneal biomechanical instability and progression to ectasia. Tomographic indices derived from elevation maps and pachymetry spatial variation produce a Belin Ambrosio display final D index (BAD-D index), which has shown better results compared to surface curvature indices for detecting very mild forms of ectasia. A logistic regression formula, integrating age, residual stromal bed, and BAD-D (Ectasia Susceptibility Score, ESS) resulted in a significant improvement in accuracy, leading to 100% sensitivity and 94% specificity for detecting susceptible cases. A comprehensive corneal structural analysis based on corneal segmental tomography can detect susceptible corneas, which increases safety for refractive surgery patients.

The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients

“…A total of 736 normal eyes (group 1), 321 KC eyes (group 2), and 113 unoperated ectatic eyes from patients with very asymmetric ectasia (group 3), who presented fellow eyes (125 eyes) with normal topographic maps (group 4), were included. Tomographic parameters included inferior-superior value (ISV), Belin-Ambrosio Display D value (BAD-D) [19,58,59], Pentacam random forest index (PRFI) [60] and the Pentacam Topographic Keratoconus classification index (TKC). Biomechanical parameters included SP A1, inverse radius, DA Ratio, and CBI.…”

Corneal ectasia detection by epithelial pattern standard deviation from OCT

“…30 The inclusion criteria for the normal group were a normal cornea in general eye examination, with an unremarkable biomicroscopic examination, corrected distance visual acuity of at least 20/20, and a normal subjective tomographic pattern, along with normal objective variables from the Belin-Ambrosio Enhanced Ectasia Display. 31 KC diagnosis was made based on clinical data considering the criteria from the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) study, along with topometric and the tomographic findings from elevation-derived corneal curvature maps. [32][33][34][35] Patients were considered as very asymmetric if the diagnosis of ectasia was confirmed in one eye (as previously described) and the fellow eye presented with normal tomographic findings, including a keratoconus percentage index score lower than 60 and a paracentral superior-inferior asymmetry lower than 1.45.…”

Automating Laser Vision Correction

“…To reduce interobserver variability or misdiagnosis we can computerize analysis using machine learning techniques [7]. Posterior corneal curvature and pachymetry data provided by Scheimpflug imaging have been investigated by Ambrosio et al [8], who showed that corneal-thickness spatial profile, corneal-volume distribution, percentage increase in thickness, and percentage increase in volume were different in keratoconic and normal eyes. Combination Placido and OCT can be automated to detect abnormal, keratoconus, suspect keratoconus, myopic post-op or normal relying on the classification by Support Vector Machine (SVM) [7], a machine learning technique screening variations against normality, and since in keratoconus such variations take place in the same position, the bulging area, to report the coincidence of the location of such variations together with corneal epithelium assessment for keratoconus detection in keratoconic eyes, epithelial thickness in the region of the cone has been reported to be thinner than that of normal eyes.…”