## 4 Discussion
### Deep Learning Techniques  
Overall, DL methods perform better than ML methods in terms of quantitative metrics, as shown in Table 1. Deep learning models are more complex than machine learning models and can better approximate the theoretically best solution. However, they are more prone to overfitting and expensive to train. One of the potential ways of enhancing the capability of these models is to include the patient's medical data such as age and gender. In the context of AD detection techniques, inclusion of patient data has shown contradictory results. While some studies have shown improvement on model performance, others have reported no difference [21,33]. Further research is needed on how to merge patient data into the model effectively. On the other hand, using images from multiple modalities has demonstrated improved performance with all methods. As an example, in [20], a DL model using OCT and fundus images performed better than a model using OCT or fundus images alone.  
The potential problem in training a deep learning model is overfitting due to limited sample size. Overfitting means the dataset size is too small for the given model capacity, and the model cannot generalize well to unseen data. There are various ways to address this, including decreasing the complexity of a model by using parameter norm penalties. Also, it is possible to increase the dataset size by collecting more data or using techniques such as data augmentation and semi-supervised learning, among others [37]. Data augmentation is used in many models to tackle limited dataset size, but this approach might lead to the generation of unrealistic features that will not be useful for predicting AD. Moreover, in many studies, pre-trained models based on natural images have been used, and they might have limited capacity for AD detection.  
One of the limitations of DL techniques is the approach taken to merge the information from different modalities. It is questionable at which level the information should be merged. The data could be merged at the hidden layer, the classifier layer, or the prediction layer. Many studies use simple techniques such as averaging or concatenating at the classifier layer. Other approaches might give better results, such as merging the activations of different images at each CNN layers [30].  
Another issue is the interpretability of DL models, which hinders the identification of biomarkers and their deployment in clinical settings. Deep learning models contain millions of parameters, and the initial input goes through many non-linear transformations. For example, the popular Resnet-50 network contains around 26 million parameters [38]. Unlike machine learning models which input hand-crafted explainable features, deep learning models complete the task in an end-to-end manner, from raw data to output prediction, making interpretability challenging. One approach to this problem is to use class activation mapping (CAM) to generate heatmaps highlighting areas of an image contributing to the decision [30]. Another technique is to investigate the correlation between explainable biomarkers and quantitative metrics derived from feature maps at different layers of the CNN. For instance, in a recent study, the neuron activation pattern (NAP) score was found to be correlated with cardiovascular risk score in a CNN model which predicts blood pressure from fundus images [39]. Apart from interpretability, there are other challenges with clinical implementation, such as the initial investment needed, regulatory compliance, etc.