# A Hybrid Artificial Intelligence Model for Detecting Keratoconus
Figure 1 shows the overall structure of a normal and KCN cornea. Artificial intelligence (AI) techniques have shown promise for addressing several ocular conditions and have achieved accuracy that equals or exceeds expert ophthalmologist [3; 4; 5; 6; 7; 8; 9; 10; 11]. Various machine learning techniques have been developed for KCN detection and refractive surgery screening [12; 13; 14; 15] and are becoming a crucial tool to aid ophthalmologists in making a better KCN diagnosis. Machine learning has been applied to discriminate normal from KCN eyes based on corneal imaging parameters [12; 15]. Most of these methods for KCN diagnosis rely on subjective evaluation of topographical maps [16]. However, automated models may provide a more accurate and objective evaluation of KCN. Conventional machine learning models such as neural networks, binary decision trees, and Zernike polynomial have been applied to corneal topography indices for KCN diagnosis [12; 17; 18; 19]. [PERSON] et al. [20] applied one of the first supervised ML models to corneal topographic maps to classify KCN. Eleven topographic indices extracted from the topographic maps were fed to neural network to classify different grades of KCN. [PERSON] et al. [21] applied ML to extract sixteen features from the four topographic maps including five indices from Pentacam measurements. The features were then fed to Support Vector Machine (SVM) and decision tree classifiers, where accuracies of 90% and 87.5% were achieved for both classifiers, respectively. Different ML techniques have been applied to corneal images collected based on OCT-based instrument (CASIA, Tomey, Japan) [18]. A support vector machine (SVM) was applied on eight corneal parameters that provided a 94% accuracy on discriminating KCN from healthy eyes. Corneal parameters from 88 patients were analyzed with machine learning to detect subclinical KCN from healthy eyes [17]. Using five parameters, the random forest method was able to achieve an area under the receiver operator characteristics curve (AUC) of 0.97 for detecting subclinical KCN. Most of these models have however used sole features from the anterior topography of corneal. Several follow up models have used features from the posterior of the cornea to evaluate KCN [22; 23; 24]. However, from the machine learning perspective, it is ideal to evaluate a comprehensive set of features and select features that are most promising in classification. Most of the current methods for automatic detection of KCN are supervised [18; 22; 23; 24; 25; 26; 27]. Some models have achieved AUC up to 0.97 using Pentacam indices only and some have combined Pentacam indices with OCT parameters and have obtained AUROCs up to 1.0 [26; 27]. Rare unsupervised models have been developed to identify KCN. Such an unsupervised approach requires no pre-labeled data avoiding expert annotation biases [28; 29; 30]. For instance, an unsupervised machine learning model was developed to identify KCN stages [28]. Principal component analysis (PCA), manifold learning, and stochastic embedding was used to cluster 4 classes of KCN for CASIA OCT imaging system. The unsupervised methods for KCN detection may address of big data labeling challenge, as medical data labeling by a human expert, is one of the challenges of applying ML for the medical field [31]. Figure 1: Overall structure of a normal and KCN cornea. The main contributions of this study are: (i) to introduce a hybrid algorithm for an unsupervised learning model and optimization techniques for KCN detection; (ii) to evaluate the performance of the algorithm for KCN detection, and (iii) to assess the model based on two independent real-world datasets from different populations. The performance of the proposed model is evaluated based on five different metrics: precision, recall, F-Score, purity, and accuracy. Furthermore, we evaluated the model based on two large dataset collected from two independent centers (Brazil and Japan).