Ординатура / Офтальмология / Английские материалы / Elevation Based Corneal Tomography 2nd_Belin, Khachikian, Ambrósio_2011

CHAPTER 9. KERATOCONUS & ECTASIA DETECTION: Asymmetric Keratoconus & Post LASIK Ectasia Study 121

Figure 15. Preoperative BAD showing anomalous CTSP and PTI lines, as well as a suspicious D index and abnormal ART values.

REFERENCES

1.Krachmer JH, Feder RS, Belin MW. Keratoconus and related noninflammatory corneal thinning disorders. Surv Ophthalmol. 1984; 28:293–322.

2.Rabinowitz YS. Keratoconus. Surv Ophthalmol. 1998; 42:297–319.

3.Belin MW, Khachikian SS. An Introduction to Understanding Elevation-Based Topography: How Elevation Data are displayed. Clin & Exp Ophthalmol. 2009; 37: 14-29

4.Wang JC, Hufnagel TJ, Buxton DF. Bilateral keratectasia after unilateral laser in situ keratomileusis: a retrospective diagnosis of ectatic corneal disorder. Cataract Refract Surg. 2003;29(10):2015-2018.

5.Klein SR, Epstein RJ, Randleman JB, Stulting RD. Corneal ectasia after laser in situ keratomileusis in patients without apparent preoperative risk factors. Cornea. 2006;25(4):388-403.

6.Binder PS. Analysis of ectasia after laser in situ keratomileusis: risk factors. J Cataract Refract Surg. 2007;33(9):1530-8.

7.Ambrósio R Jr, Alonso RS, Luz A, Velarde LGC. Corneal-thickness spatial profile and corneal-volume distribution: Tomographic indices to detect keratoconus. J Cataract Refract Surg. 2006; 32: 18511859.

8.Li X, Rabinowitz YS, Rasheed K, Yang H. Longitudinal study of the normal eyes in unilateral keratoconus patients. Ophthalmology. 2004;111(3):440-446.

9.Ambrósio R Jr, Klyce SD, Wilson SE. Corneal topographic and pachymetric screening of keratorefractive patients. J Refract Surg. 2003;19(1):24-29.

10.Ambrósio R Jr, Dawson DG, Salomão M, Guerra FP, Caiado AL, Belin MW. 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.

11.Ambrósio R Jr, Nogueira LP, Caldas DL, Fontes BM, Luz A, Cazal JO, Alves MR, Belin MW. Evaluation of corneal shape and biomechanics before LASIK. Int Ophthalmol Clin. 2011;51:11-38.

12.Ambrósio Jr R, Caiado ALC, Guerra FP, Louzada R, Roy AS, Luz A, Dupps WJ, Belin MW. Novel Pachymetric Parameters Based on Corneal Tomography for Diagnosing Keratoconus. J Refract Surg. 2011; article in press.

13.Ciolino JB, Belin MW. Changes in the posterior cornea after laser in situ keratomileusis and photorefractive keratectomy. J Cataract Refract Surg 2006;32:1426-31.

14.Ciolino JB, Khachikian SS, Cortese MJ, Belin MW. Long-term stability of the posterior cornea after laser in situ keratomileusis. J Cataract Refract Surg. 2007;33:1366-70

for Ectasia

João Marcelo Lyra, MD, PhD

Aydano P. Machado, MSc, PhD

Bruna V. Ventura, MD

Guilherme Ribeiro, MD

Luana P. N. Araújo, MD

Isaac Ramos, MD

Frederico P. Guerra, MD

Renato Ambrósio Jr, MD, PhD

ADVANCES IN REFRACTIVE SURGERY SCREENING

The emergence of progressive ectasia as a rare but major complication following keratorefractive surgery led to the need for preoperative identification of mild and/or subclinical forms of ectasia. Even with rigorous an detailed screening methods, some patients may still develop post-Laser in situ Keratomileusis (LASIK) ectasia of unknown etiology.1,2 The goal of refractive surgical screening is to improve sensitivity, not only in cases that have mild keratoconus, but ultimately to develop the ability to identify those patients with susceptibility or predisposition to develop ectasia.1 Advances in tomographic and biomechanical corneal characterization provide more data to characterize the cornea beyond just corneal front surface topography and central corneal thickness.

The complexity and uniqueness of these newer diagnostic methods, however, has made their interpretation a major challenge for refractive surgeons.1 It is critical that surgeons understand the origin and meaning of these new parameters. The interpretation of any diagnostic parameter should be considered in face of normal values, as well as on the distribution of these values among cases that are known to have disease. Often the combination of multiple parameters will exceed the diagnostic ability of any single measurement and provide the bases for clinical decision making. This chapter describes how we can use Artificial Intelligence (AI) techniques for combining metrics generated from corneal tomography for screening ectasia risk among refractive candidates.

WHAT IS ARTIFICIAL INTELLIGENCE?

Artificial intelligence is a specific area of computational science that creates systems that act or aim to reproduce human reasoning. They were designed to perform massive data analysis that would be relatively impossible for humans. The studies in this field began in the 1950s. Since then, substantial developments have come to this area, culminating in the several “intelligent” systems available today.

In medicine, and specifically in Ophthalmology, AI has been used for several purposes, including keratoconus diagnosis, photorefractive keratectomy nomograms, and automatic recognition of cell layers in corneal confocal microscopy images, among others.3-13 AI can be used to help answer clinically relevant questions, such as what are the characteristics that should be considered to improve the screening process for ectasia risk among refractive candidates. Our goal in using AI is to make the screening process to detect ectasia easier, more objective and more accurate, increasing both sensitivity and specificity.

To reach this level of knowledge, we have to gather a valid patient dataset, which has to include both normal and abnormal eyes. The screening process (AI program) should be trained not only to detect moderate to advanced cases of keratoconus, but also to detect the mildest or sub-clinical forms of the disease and even its predisposition or susceptibility. Thus, to enable the detection of sub-clinical disease, we have to include such cases to train the AI system. The inclusion criteria for such cases, however, have been relatively controversial. We believe that such cases should not be selected exclusively based on data from anterior corneal topography (front surface curvature map), but should include patient history, data from the contra-lateral eye and full tomographic parameters. The fellow eye with relatively normal topography from a patient with unquestionable keratoconus in the other eye can provide critical data to assist in identifying early disease (FIGURES 1A and 1B).7

Besides FFK, the preoperative status of a patient that developed post LASIK ectasia, particularly those that have been reported as “normal” based on anterior curvature and CCT, also represent critical data for the AI program (FIGURES 2A and 2B).

Along with an appropriate patient dataset, it is critical to have multiple reliable variables for insure that the training procedure of the AI techniques is accurate and valid. Considering the potential volume of data (curvature, elevation, pachymetry, biomechanics, family history, symmetry, etc.) such analysis would be very difficult for a human.

CHAPTER 10. ARTIFICIAL INTELLIGENCE TECHNIQUES FOR IMPROVING TOMOGRAPHIC SCREENING FOR ECTASIA 127

LINEAR MODEL

In Linear Models, the combination of the information is performed using linear functions (first order equations), and unknown model parameters are estimated from the dataset. The Belin/Ambrósio Display (BAD II) was developed based on a regression analysis that evaluated 51 eyes with mild to moderate keratoconus and 198 normal eyes. The BAD included the data from enhanced elevation from the front (df) and back (db) corneal surfaces, from pachymetric distribution (dp), from the thinnest point (dt) and from the vertical deviation of the thinnest from the apex (dy).1,14,15 Each parameter was confirmed to have a normal distribution on the Kolmogorov Smirnov test and was normalized based on the mean and standard deviation values obtained in a normal population. So the final “D” is a result of the combination of df, db, dp, dt and dy using a first order equation that input different weights for each parameter.

In order to improve the sensitivity and specificity of the final BAD “D” parameter, we tested different parameters and used the most relevant ones in order to create a linear model using machine learning techniques. A second dataset consisted of 451 normal eyes (including 268 cases with stable LASIK over one year) and 112 abnormal eyes: 81 eyes randomly selected from 81 patients with keratoconus (KC), 20 eyes with FFK, contra-lateral eyes with normal topography from patients with asymmetric keratoconus and preoperative eyes of 11 patients that developed ectasia after LASIK.

Along with the parameters originally included in the second generation of the BAD (Df, Db, Dp, Dt, Dy), the following parameters were added to the BAD III analysis: anterior and posterior elevation at the thinnest point considering the best fit surface (BFS) for 8mm zone, Relational Thickness to the Maximal (ART Max), and average (ART Avg) pachymetric progression, maximal keratometric value in the sagittal (or axial) map, and the minimal relative pachymetric value. These parameters were normalized based on the mean and standard deviation from a normal population. The inclusion of more variables had the goal to enable the generation of combined parameters that would have a better performance than the current (second generation) D from the BAD (version 2 or BAD 2).

128 ELEVATION BASED CORNEAL TOMOGRAPHY

An example of one tested linear regression analysis formula combining machine learning techniques is shown below.

0.034 * Df + 0.012 * Db + 0.141 * Dp + 0.051 * Dt + 0.054 * Dy + 0.039 * Ele B BFS 8mm Thinnest

This linear model had an accuracy of 96.59%, a sensitivity of 93.21% (151 of 162 cases) and a specificity of 94.68 % (427 of 451) (TABLE 1).

TABLE 1 - Sensitivity and specificity for keratoconus diagnosis (161 keratoconus/susceptible eyes and 451 normal eyes)

Another formula that combined combining machine learning techniques and clinical expertise was:

se0.117 * Df + 0.106 * Db + 0.126 * Dp + 0.127 * Dt + 0.100 * Dy + 0.120 * Ele B BFS 8mm Thinnest + 0.100 * ART Max + 0.100 * ART Avg + 0.100 * K Max Front D + 0.100 * Rel Pachy Min + 0.97

This second linear model had an accuracy of 96.25%, a sensitivity of 93.87% (153 of 163 cases) and a specificity of 97.12 % (438 of 451) (TABLE 2).

TABLE 2 - Sensitivity and specificity for keratoconus diagnosis (161 keratoconus/susceptible eyes and 451 normal eyes).