Enhanced Diagnostics for Corneal Ectatic Diseases: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Louise Pellegrino Gomes Esporcatte.

There are different fundamental diagnostic strategies for patients with ectatic corneal diseases (ECDs): screening, confirmation of the diagnosis, classification of the type of ECD, severity staging, prognostic assessment, and clinical follow-up. The conscious application of such strategies enables individualized treatments. The need for improved diagnostics of ECD is related to the advent of therapeutic refractive procedures that are considered prior to keratoplasty.

  • keratoconus
  • corneal ectasia
  • multimodal corneal imaging

1. Introduction

Keratoconus (KC) is the most common ectatic corneal disease (ECD), which comprises a class of disorders characterized by progressive thinning and subsequent bulging of the cornea, causing irregular astigmatism [1,2][1][2]. When evaluating patients with ectatic disorders of the cornea, the clinician must consider the different fundamental diagnostic strategies (Table 1): screening, confirmation of the diagnosis, classification of the type of ectasia, staging severity, prognostic evaluation, and clinical follow-up for individualized treatments. Refractive surgery boosted extensive developments in diagnostic and therapeutic technologies for ECD [3,4,5,6][3][4][5][6]. Therapeutic refractive procedures are less invasive alternatives to keratoplasty. Corneal crosslinking, customized ablations, and intracorneal ring segments (ICRSs) can be indicated for such patients. Introducing such options for managing ECD determined the need for an improved disease evaluation.
Table 1. Diagnostic strategies in keratoconus.
Diagnostic Strategies What Is? How?
Screening Detect mild forms of KC, and ectasia susceptibility, considering the refractive treatment and the impact on the cornea. Placido-disk corneal topography, Scheimpflug tomography, OCT (or VHF US) segmental tomography, and biomechanical assessments.
Diagnostic confirmation Paradigm shift related to the management of ECD and access ectasia risk and progression to improve treatment. Comprehensive clinical evaluation with multimodal imaging.
Classification of ectasia Group of disorders characterized by progressive thinning and following protruding of the corneal structure. Integration of tomographic and biomechanical data with AI, genetics, and molecular biology.
Staging To prevent visual loss before it even occurs, with new treatment modalities. ABCD + E ectasia/KC staging.
Prognostic Management of KC varies depending on the severity of the disease Biomechanical parameters (SPA-1) and patient compliance.
Besides the paradigm shift in managing such patients, enhancing the sensitivity to detect very mild forms of ECD is relevant for identifying patients at higher risk for progressive iatrogenic ectasia after laser vision correction (LVC). The current concept for assessing ectasia risk before LVC combines the characterization of corneal properties, related to the inherent susceptibility for ectasia progression, and the impact of the LVC procedure on the corneal structure. External mechanical factors, such as eye rubbing and pressurizing the eyes during sleep, also play a significant role. This concept contemplates the two-hit hypothesis for the pathogenesis of ECD [7].
Multimodal refractive imaging involves different technologies, including Placido-disk corneal topography, Scheimpflug 3-D tomography, segmental or layered tomography with layered epithelial thickness using OCT (optical coherence tomography), digital very high-frequency ultrasound (VHF-US), and ocular wavefront. Corneal biomechanical assessments were translated from mathematical models. In vitro laboratory destructive tests to clinically measure beyond shape analysis have been promising as a crucial tool for enhancing the accuracy of identifying mild forms of ECD and the capacity to characterize ectasia susceptibility [7,8][7][8].
Artificial intelligence (AI) has proven its relevance in integrating the overabundance of data generated for facilitating clinical decisions. Genetic and molecular biology tests are promising to further enhance diagnostic accuracy for allowing personalized treatments. Moreover, the hypothesis that mild or fruste keratoconus is a risk factor for a mother delivering a baby with Down’s syndrome opened a new horizon for the relevance of the diagnostics of ECD [9].

2. Multimodal Imaging

The concept of multimodal corneal imaging was announced to integrate several diagnostic tools offered for corneal and anterior segment imaging (Table 2) [5]. Placido disk-based corneal topography improved the ability to detect abnormalities in mild corneal ectasia, even in patients with normal distance-corrected visual acuity and slit-lamp examinations within normality [4,10,11][4][10][11].
Table 2. Exams for multimodal propaedeutics in keratoconus.
Imaging Tests Characterization
Corneal Topography Analysis of the front surface of the cornea using Placido-disk-based reflection.
Corneal Tomography Three dimensional reconstruction of the cornea enables the calculation of elevation maps of the front and back surfaces, along with a pachymetric map, typically with rotating Scheimpflug imaging.
Segmental Corneal Tomography Tomographic evaluation of segments of the cornea, including epithelium, Bowman’s layer, and Descemet’s membrane.
Corvis ST Non-contact tonometer system that uses an ultra-high-speed Scheimpflug camera to monitor the corneal deformation response over a 5–6 mm area during a constant application of an air pulse, allowing for a more detailed assessment of the deformation process.
Anterior segment tomography with 3D reconstruction of the cornea offered more detail about corneal architecture with a range of quantitative indices derived from the front and back elevation along with pachymetric maps [5,12,13,14][5][12][13][14]. Different studies involving eyes with typically “innocent” topography, from patients with clinical ectasia identified in the fellow eye, confirmed the capacity of corneal tomography to improve the accuracy of detecting mild or subclinical ectatic disease [4,15,16,17,18,19,20][4][15][16][17][18][19][20]. Corneal tomographic parameters revealed an excellent ability to identify susceptibility to developing ectasia after LASIK (Laser-Assisted in Situ Keratomileusis) in retrospective studies involving patients with such complications [13,21,22][13][21][22]. The need to go beyond corneal shape evaluation for describing ectasia risk within the biomechanical domain was supported and promoted [23,24][23][24].

3. Screening for Ectasia Risk before Laser Vision Correction

The key to refractive surgery screening is to identify cases with mild ectasia and cases of high susceptibility or predisposition for biomechanical failure and ectasia after LVC [25,26][25][26]. Corneal ectasia has been a severe complication of LASIK surgery since the first report by Seiler in 1998 [27]. Clinical risk factors associated with ectasia include preoperative ectactic corneal disease, young age, and low preoperative pachymetry [21,28,29,30][21][28][29][30]. Corneal ectasia is the consequence of a biomechanical decompensation of the stroma. This could be related to either the impact of the procedure on the corneal structure or the preoperative individual biomechanical properties. The present understanding is that a combination of these factors determines stability or progression of ectasia after LVC [7,8,31][7][8][31]. However, some cases with low preoperative risk factors can develop ectasia, whereas others with high probabilities of developing ectasia continue to be stable [7]. Long-term stability after LVC is determined by the preoperative biomechanical strength of the patient’s corneal stroma, the amount of biomechanical alteration caused by the surgery, and the postoperative stress load on the cornea [31].

3.1. Corneal Topography

Placido disk-based corneal topography projects a sequence of concentric rings onto the anterior corneal surface and uses quantitative data to generate color-coded maps [32,33][32][33]. For the diagnosis of KC, one of the most commonly used indices is Rabinowitz and McDonnell’s [34]. Corneal topography epitomizes a revolution in corneal imaging. It has the sensitivity to detect ectatic disease before any loss of best-corrected visual acuity, and any significant slit-lamp examination findings acquired [10,11][10][11]. Consequently, corneal topography is considered an essential examination when screening refractive surgery candidates [35]. Randleman and co-workers developed the Ectasia Risk Scoring System with corneal topography, pachymetric measurements, and clinical variables [29,36][29][36]. However, limitations of topography examination were documented after literature presented patients who developed post-LVC ectasia, despite normal preoperative anterior surface maps [7,28,37][7][28][37] and patients with renowned risk factors and abnormal topographic maps who stayed stable years after LVC [38]. Therefore, there is a need for a complete characterization of the cornea to improve screening for ectasia susceptibility of refractive candidates.

3.2. Corneal Tomography

The Pentacam (Oculus, Wetzlar, Germany) uses a rotating Scheimpflug camera and a frontal view illumination system to reconstruct topographic images of the cornea and anterior segment, and diverse indices have been proposed to increase the diagnosis of KC. The Pentacam Belin–Ambrósio Enhanced Ectasia Display (BAD) was designed as a clinical tool to assist clinical diagnosis of KC and ECD [15,16,17,19,39][15][16][17][19][39]. Tomographic parameters are displayed as the standard deviation from normality to disease (d values), including front and back elevation at the thinnest point, change in anterior and posterior elevation of the standard and enhanced best-fit sphere (BFS), the thinnest value and its vertical location, pachymetric progression index, Ambrósio’s relational thickness, and maximal curvature (KMax). A final ‘D’ value is calculated based on linear regression analysis, which weights each parameter differently [8,14,15][8][14][15]. Lopes and collaborators proposed using random forest (RF) to enhance pattern recognition [40]. The Pentacam Random Forest Index (PRFI) was developed to detect ectatic corneal disease among the following groups, performing with better accuracy than any individual tomographic parameter: normal eyes (stable LASIK cases), clinical KC, normal topographic eyes with very asymmetric ectasia (VAE-NT), and eyes with higher ectasia susceptibility (preoperative data of post LASIK ectasia). Compared with the BAD-D, which correctly classified only 55.3% of the post-LASIK ectasia, the PRFI correctly detected 80%. Considering the recognition of the VAE-NT, the PRFI presented a sensitivity of 85.2% and specificity of 96.6% in the independent test set [40].

3.3. Segmental Corneal Tomography

Corneal epithelial indices for identifying KC were developed with this technology, and studies proposed this approach as a valuable instrument in identifying milder forms of the disease [41,42][41][42]. Working with optical coherence tomography (OCT) technology, Huang and collaborators developed a similar approach with an extended epithelial thickness map and different indices to detect KC in its initial stages [43,44][43][44]. Sinha-Roy and coauthors also investigated the irregularity of the Bowman’s layer in normal and ectatic corneas and proposed a new Bowman’s roughness index. This index had a good performance in detecting KC and, when combined with the BAD-D and epithelial thickness data, improved the sensitivity for detecting mild forms of ectasia [45]. Studies also demonstrated the use of this technology to investigate flap thickness reproducibility, to understand corneal refractive surgery complications, and to measure epithelial changes after refractive surgery [46].

References

  1. Krachmer, J.H.; Feder, R.S.; Belin, M.W. Keratoconus and related noninflammatory corneal thinning disorders. Surv. Ophthalmol. 1984, 28, 293–322.
  2. Rabinowitz, Y.S. Keratoconus. Surv. Ophthalmol. 1998, 42, 297–319.
  3. Sedaghat, M.R.; Ostadi-Moghadam, H.; Jabbarvand, M.; Askarizadeh, F.; Momeni-Moghaddam, H.; Narooie-Noori, F. Corneal hysteresis and corneal resistance factor in pellucid marginal degeneration. J. Curr. Ophthalmol. 2018, 30, 42–47.
  4. Ambrosio, R., Jr.; Correia, F.F.; Lopes, B.; Salomao, M.Q.; Luz, A.; Dawson, D.G.; Elsheikh, A.; Vinciguerra, R.; Vinciguerra, P.; Roberts, C.J. Corneal biomechanics in ectatic diseases: Refractive surgery implications. Open Ophthalmol. J. 2017, 11, 176–193.
  5. Salomão, M.; Hoffling-Lima, A.L.; Lopes, B.; Belin, M.W.; Sena, N.; Dawson, D.G.; Ambrósio, R., Jr. Recent developments in keratoconus diagnosis. Expert Rev. Ophthalmol. 2018, 13, 329–341.
  6. Esporcatte, L.P.G.; Salomao, M.Q.; Lopes, B.T.; Vinciguerra, P.; Vinciguerra, R.; Roberts, C.; Elsheikh, A.; Dawson, D.G.; Ambrosio, R., Jr. Biomechanical diagnostics of the cornea. Eye Vis. 2020, 7, 9.
  7. Ambrósio, R., Jr.; Dawson, D.G.; Salomão, M.; Guerra, F.P.; Caiado, A.L.C.; Belin, M.W. Corneal ectasia after LASIK despite low preoperative risk: Tomographic and biomechanical findings in the unoperated, stable, fellow eye. J. Refract Surg. 2010, 26, 906–911.
  8. Ambrósio, R., Jr.; Nogueira, L.P.; Caldas, D.L.; Fontes, B.M.; Luz, A.; Cazal, J.O.; Alves, M.R.; Belin, M.W. Evaluation of corneal shape and biomechanics before LASIK. Int. Ophthalmol. Clin. 2011, 51, 11–38.
  9. Hashemi, H.; Asgari, S.; Panahi, P.; Mehravaran, S.; Fotouhi, A.; Ambrosio, R., Jr. Corneal ectasia in mothers of down syndrome children. Sci. Rep. 2021, 11, 22436.
  10. Maeda, N.; Klyce, S.D.; Smolek, M.K.; Thompson, H.W. Automated keratoconus screening with corneal topography analysis. Investig. Ophthalmol. Vis. Sci. 1994, 35, 2749–2757.
  11. Maguire, L.J.; Bourne, W.M. Corneal topography of early keratoconus. Am. J. Ophthalmol. 1989, 108, 107–112.
  12. Ambrósio, R., Jr.; Alonso, R.S.; Luz, A.; Velarde, L.G.C. Corneal-thickness spatial profile and corneal-volume distribution: Tomographic indices to detect keratoconus. J. Cataract Refract Surg. 2006, 32, 1851–1859.
  13. Ambrósio, R., Jr.; Caiado, A.L.C.; Guerra, F.P.; Louzada, R.; Roy, A.S.; Luz, A.; Dupps, W.J.; Belin, M.W. Novel pachymetric parameters based on corneal tomography for diagnosing keratoconus. J. Refract Surg. 2011, 27, 753–758.
  14. Lopes, B.T.; Ramos, I.C.; Dawson, D.G.; Belin, M.W.; Ambrosio, R., Jr. Detection of ectatic corneal diseases based on pentacam. Z. Für Med. Phys. 2016, 26, 136–142.
  15. Ambrosio, R., Jr.; Valbon, B.F.; Faria-Correia, F.; Ramos, I.; Luz, A. Scheimpflug imaging for laser refractive surgery. Curr. Opin. Ophthalmol. 2013, 24, 310–320.
  16. Smadja, D.; Touboul, D.; Cohen, A.; Doveh, E.; Santhiago, M.R.; Mello, G.R.; Krueger, R.R.; Colin, J. Detection of subclinical keratoconus using an automated decision tree classification. Am. J. Ophthalmol. 2013, 156, 237–246.e231.
  17. Saad, A.; Gatinel, D. Topographic and tomographic properties of forme fruste keratoconus corneas. Investig. Ophthalmol. Vis. Sci. 2010, 51, 5546–5555.
  18. Hwang, E.S.; Perez-Straziota, C.E.; Kim, S.W.; Santhiago, M.R.; Randleman, J.B. Distinguishing highly asymmetric keratoconus eyes using combined scheimpflug and spectral-domain OCT analysis. Ophthalmology 2018, 125, 1862–1871.
  19. Luz, A.; Lopes, B.; Hallahan, K.M.; Valbon, B.; Ramos, I.; Faria-Correia, F.; Schor, P.; Dupps, W.J.; Ambrosio, R., Jr. Enhanced combined tomography and biomechanics data for distinguishing forme fruste keratoconus. J. Refract Surg. 2016, 32, 479–494.
  20. Golan, O.; Piccinini, A.L.; Hwang, E.S.; De Oca Gonzalez, I.M.; Krauthammer, M.; Khandelwal, S.S.; Smadja, D.; Randleman, J.B. Distinguishing highly asymmetric keratoconus eyes using dual scheimpflug/placido analysis. Am. J. Ophthalmol. 2019, 201, 46–53.
  21. Ambrosio, R., Jr.; Belin, M. Enhanced screening for ectasia risk prior to laser laser vision correction. Int. J. Keratoconus Ectatic Corneal Dis. 2017, 6, 23–33.
  22. Ambrósio Junior, R.; Caldas, D.L.; Silva, R.S.d.; Pimentel, L.N.; Valbon, B.d.F. Impacto da análise do “wavefront” na refratometria de pacientes com ceratocone. Rev. Bras. Oftalmol. 2010, 69, 294–300.
  23. Belin, M.W.; Khachikian, S.S.; Salomão, M.; Ambrósio, R., Jr. Keratoconus and ectasia detection based on elevation dat with the Oculus Pentacam®. In Corneal Topography from Theory to Practice; Kiliç, A., Roberts, C.J., Eds.; Kugler Publications: Amsterdam, The Netherlands, 2013; p. 167.
  24. Luz, A.; Faria-Correia, F.; Salomão, M.Q.; Lopes, B.T.; Ambrósio, R., Jr. Corneal biomechanics: Where are we? J. Curr. Ophthalmol. 2016, 28, 97.
  25. Ambrosio, R., Jr.; Randleman, J.B. Screening for ectasia risk: What are we screening for and how should we screen for it? J. Refract Surg. 2013, 29, 230–232.
  26. Belin, M.W.; Ambrosio, R., Jr. Corneal ectasia risk score: Statistical validity and clinical relevance. J. Refract Surg. 2010, 26, 238–240.
  27. Seiler, T.; Quurke, A.W. Iatrogenic keratectasia after LASIK in a case of forme fruste keratoconus. J. Cataract. Refract Surg. 1998, 24, 1007–1009.
  28. Klein, S.R.; Epstein, R.J.; Randleman, J.B.; Stulting, R.D. Corneal ectasia after laser in situ keratomileusis in patients without apparent preoperative risk factors. Cornea 2006, 25, 388–403.
  29. Randleman, J.B.; Woodward, M.; Lynn, M.J.; Stulting, R.D. Risk assessment for ectasia after corneal refractive surgery. Ophthalmology 2008, 115, 37–50.
  30. Valbon, B.F.; Ambrosio, R., Jr.; Glicéria, J.; Santos, R.; Luz, A.; Alves, M.R. Unilateral corneal ectasia after Bilateral LASIK: The thick flap counts. Int. J. Keratoconus Ectatic Corneal Dis. 2013, 2, 79.
  31. Zarei-Ghanavati, S.; Hassandazadeh Ambrosio, R., Jr. Corneal Ecatasia after Lase-Assisted Small-Incision Lenticule Extraction: The Case for an Enhanced Ectasia Risk-Assessment. J. Curr. Ophthamlmol. 2022, 34, 357–363.
  32. Wilson, S.E.; Lin, D.T.; Klyce, S.D. Corneal topography of keratoconus. Cornea 1991, 10, 2–8.
  33. Wilson, S.E.; Ambrosio, R., Jr. Computerized corneal topography and its importance to wavefront technology. Cornea 2001, 20, 441–454.
  34. Rabinowitz, Y.S.; McDonnell, P.J. Computer-assisted corneal topography in keratoconus. Refract Corneal Surg. 1989, 5, 400–408.
  35. Ambrosio, R., Jr.; Klyce, S.D.; Wilson, S.E. Corneal topographic and pachymetric screening of keratorefractive patients. J. Refract Surg. 2003, 19, 24–29.
  36. Randleman, J.B.; Trattler, W.B.; Stulting, R.D. Validation of the ectasia risk score system for preoperative laser in situ keratomileusis screening. Am. J. Ophthalmol. 2008, 145, 813–818.
  37. Malecaze, F.; Coullet, J.; Calvas, P.; Fournie, P.; Arne, J.L.; Brodaty, C. Corneal ectasia after photorefractive keratectomy for low myopia. Ophthalmology 2006, 113, 742–746.
  38. Reinstein, D.Z.; Archer, T.J.; Gobbe, M. Stability of LASIK in topographically suspect keratoconus confirmed non-keratoconic by Artemis VHF digital ultrasound epithelial thickness mapping: 1-year follow-up. J. Refract Surg. 2009, 25, 569–577.
  39. Belin, M.W.; Ambrosio, R., Jr. Scheimpflug imaging for keratoconus and ectatic disease. Indian J. Ophthalmol. 2013, 61, 401–406.
  40. Lopes, B.T.; Ramos, I.C.; Salomao, M.Q.; Guerra, F.P.; Schallhorn, S.C.; Schallhorn, J.M.; Vinciguerra, R.; Vinciguerra, P.; Price, F.W.; Price, M.O.; et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am. J. Ophthalmol. 2018, 195, 223–232.
  41. Reinstein, D.Z.; Archer, T.J.; Urs, R.; Gobbe, M.; RoyChoudhury, A.; Silverman, R.H. Detection of keratoconus in clinically and algorithmically topographically normal fellow eyes using epithelial thickness analysis. J. Refract Surg. 2015, 31, 736–744.
  42. Reinstein, D.Z.; Gobbe, M.; Archer, T.J.; Silverman, R.H.; Coleman, D.J. Epithelial, stromal, and total corneal thickness in keratoconus: Three-dimensional display with artemis very-high frequency digital ultrasound. J. Refract Surg. 2010, 26, 259–271.
  43. Li, Y.; Chamberlain, W.; Tan, O.; Brass, R.; Weiss, J.L.; Huang, D. Subclinical keratoconus detection by pattern analysis of corneal and epithelial thickness maps with optical coherence tomography. J. Cataract Refract Surg. 2016, 42, 284–295.
  44. Li, Y.; Tan, O.; Brass, R.; Weiss, J.L.; Huang, D. Corneal epithelial thickness mapping by Fourier-domain optical coherence tomography in normal and keratoconic eyes. Ophthalmology 2012, 119, 2425–2433.
  45. Pahuja, N.; Shroff, R.; Pahanpate, P.; Francis, M.; Veeboy, L.; Shetty, R.; Nuijts, R.M.; Roy, A.S. Application of high resolution OCT to evaluate irregularity of Bowman’s layer in asymmetric keratoconus. J. Biophotonics 2017, 10, 701–707.
  46. Salomão, M.Q.; Hofling-Lima, A.L.; Lopes, B.T.; Canedo, A.L.C.; Dawson, D.G.; Carneiro-Freitas, R.; Ambrósio, R., Jr. Role of the corneal epithelium measurements in keratorefractive surgery. Curr. Opin. Ophthalmol. 2017, 28, 326–336.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback