Epistemological Borders as Cosmopolitan Method

In our third example, we use a learning machine technique that is based on our neural system. The basic idea is to create artificial neurons that will be linked in an artificial neural network (FIGURE 5) in order to simulate processing of neural information in humans. These are adaptable systems with the ability to learn new tasks, to err, to execute generalizations and to infer new knowledge (i.e. to learn from past mistakes). As a machine learning technique, it is more complex than the algorithms used in the previous two examples. Thus, it can give us better results with respect to keratoconus diagnosis and predisposition to ectasia.

There are many types of neural networks, of which we will use two to illustrate our discussion. The first network is a radial basis function neural network (RBF), and the second is a multilayer perceptron (MLP). A fundamental difference between the two networks is that the RBF has only one hidden layer of neurons and uses radial basis functions.

We used the same database of eyes to test both techniques: 451 normal, 20 forme-fruste, 11 post-LASIK ectasia and 132 keratoconus. In order to show the MLP results we merged the forme fruste and ectasia cases as one group: susceptible cases (TABLE 4). The MLP was able to correctly classify 30 of 31 susceptible cases (96.77%). In FIGURE 6, we used the ROC curve to compare the actual BAD version to the MLP. The AUC of MLP was 0.0810 greater than actual BAD with a statistically significant difference (P = 0.0482).

Figure 6. ROC curve: actual BAD x MLPn for forme fruste and post-LASIK ectasia (31 susceptible eyes and 451 normal eyes).

CLASSIFIED AS

TABLE 4 - MLP classifying eyes as susceptible to ectasia or normal (31 susceptible eyes and 451 normal eyes)

Figure 7. ROC curve: actual BAD x RBF/BrAIn for KC (132 keratoconus eyes and 451 normal eyes).

On the other hand, the RBF correctly classified 441 of 451 normal eyes (95.45%) and 126 of 132 KC cases (97.78%) (TABLE 5). In FIGURE 7 we can notice the difference in ROC curve between the actual BAD and the RBF. The AUC of the RBF was 0.0261 greater than actual BAD with a statistically significant difference (P = 0.4852).

As the two techniques work differently, one is not necessarily better than the other.

Rather, in specific cases, an MLP will be a better option than RBF and vice versa. In our cases we observed this exact situation. The MLP was better suited for the detection of predisposition to ectasia, while the RBF was more applicable to the diagnosis of keratoconus.

CLASSIFIED AS

TABLE 5 - BAD RBF classifying eyes as KC or normal (132 keratoconus eyes and 451 normal eyes).

CONCLUSIONS

This chapter describes the ongoing study with machine learning algorithms for screening of refractive surgery patients using tomographic parameters. The combination of AI and clinician expertise is the best strategy for improving pre-operative refractive screening. This approach will further enhance the Belin/Ambrósio Display (BAD) analysis. Our results have shown that it is possible to improve the performance of ectasia screening using more complex algorithms such as the ones using decision trees, MLP and RFB and achieve higher sensitivity and specificity than previously possible.

We predict different directions for future work on this area, including, incorporating more parameters such as the ones derived from Zernike wavefront analysis of corneal shape.

With more parameters, it will be necessary to apply an attribute selection algorithm in order to remove irrelevant or redundant tomographic parameters. It is also important to expand the database of cases that develop ectasia, but this task is not simple since we need the pre-operative data from such cases which is rarely available or retrievable. Finally, the integration of corneal tomography data with biomechanical parameters may have potential to build an even more accurate AI program that may be relevant not only for ectasia risk screening but also for improving refractive surgery planning.

ACKNOWLEDGMENT

The work performed in this chapter is largely the product of Brazilian Study Group of Artificial Intelligence and Corneal Analysis.

REFERENCES

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Familiarity with the elevation maps is the first step in being able to recognize subtle pathology. As noted previously, pattern recognition is the quickest and simplest way to perform efficient patient screening. The most useful map for the physician is one that displays the most clinically relevant information without being unnecessarily confusing. While the Pentacam offers a plethora of maps, we routinely recommend the 4-map refractive composite display for almost all screening situations. This can be augmented by the Belin/Ambrosio display to help differentiate questionable cases of keratoconus or ectasia.

The 4-map refractive display shows anterior and posterior elevation, anterior sagittal curvature, corneal thickness (pachymetry) and a number of specific indices such as steep and flat axis, average K, and pachymetry at the corneal apex, center of pupil and thinnest portion of the cornea. Our recommended settings for this display have been discussed in detail in the chapter on display parameters.

The following maps are presented to help the reader recognize the diversity of patterns seen in both normal and abnormal eyes. We have tried to keep the layout of each figure uniform. While the majority of the maps come from our own practice, some of the maps originate from other sources. The location of the individual maps (e.g. elevation, curvature, corneal thickness) may vary due to outside individual practice preferences.

Chapter