## 2 Related Work  
There has been some work related to ophthalmology in which Shapley values have been used. One of these works is [13], where a study was conducted to determine the degree of agreement of 14 medical specialists with the results of 13 attribution methods applied to a neural network based on InceptionV3 to diagnose choroidal neovascularization, diabetic macular edema and drusen from retinal images captured with an OCT. The results show that the three methods most highly rated by clinicians were Deep Taylor, Guided backpropagation and Deep SHAP. Another publication, also related to this field, is that of [14]. In this work, the authors train a convolutional neural network model based on DensetNet121 to detect different degrees of diabetic retinopathy in fundus images. In order to explain the results, they choose to use Grad-CAM and Deep SHAP. As noted in the introduction, Deep SHAP is an approximate way to calculate Shapley values in Deep Learning systems, making the calculation feasible from a computational standpoint but with some disadvantages:  
* It's approximate: Built on DeepLIFT [15], it estimates rather than computes exact Shapley values, which can cause inaccuracies, especially in complex models. * Feature independence is assumed: It doesn't handle feature correlations well, which may lead to misleading explanations. * Sensitive to background data: The quality of explanations hinges heavily on the choice of representative background samples. * Model limitations: Only works with differentiable models that support backpropagation, so it is a no-go for many non-standard architectures. * Harder to generalize: Local explanations don't always aggregate neatly into global insights, making interpretation tricky. * Can still be resource-hungry: While faster than some alternatives, it can still be taxing on large-scale models. The proposed method is much more straightforward, as it is based solely on discretizing the problem using image sectors and devising a strategy to hide those sectors in order to retrain the model. Focusing now on glaucoma diagnosis, in [16] several models were developed, some based on clinical features to establish a baseline from which to start, and another deep learning model that combines information from macular OCT and color eye fundus images with the demographic, systemic, and ocular data that were used in the baseline models. For the interpretability of models using images, they used Integrated gradients and SmoothGrad, while for tree-based models developed with XGBoost they calculated SHAP values to assess the importance and interaction between features. A similar approach is followed in [17] using also OCT and SHAP values to develop a valuable user-friendly XAI software tool which provides transparent and interpretable insights to improve decision-making. In the work carried out by [18], they collected 23 features, such as intraocular pressure, a visual field test, a retinal nerve fiber layer (RNFL) test performed with OCT and a fundus photograph, and carried out a feature selection process to finally keep only five of them. With the selected features, they tested several algorithms, such as support vector machines (SVM), C5.0, random forests, or XGBoost, and evaluated the importance of these features in the final decision using different graphs, including those derived from SHAP. Also related to glaucoma, but using only structured data extracted from the electronic health record (EHR), we find the work of [19], where they train different models based on regression, decision trees or deep learning to predict whether a glaucoma patient would need surgery, based on progression data extracted from the follow-up of 4512 patients. They extracted 361 features from their medical records, including demographic, eye exam, diagnostic and prescription data.