## 2 Related Works  
This section investigates primarily the application of Siamese Neural Networks (SNNs) in the context of utilizing medical images for longitudinal disease evaluation. As a secondary objective, we extend our focus on the use of SNNs within the medical domain with the aim of gaining insights into their architectural adaptations that are more suited to our specific research objectives. [PERSON] et al. [24] used a Siamese Neural Network to monitor and assess the severity and progression of medical imaging in two specific diseases: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. The technique employed measures the similarity between two images captured at different time points. As no severity ranking labels were available, the authors computed a median Euclidean distance from a set of known normal images. Their approach involved using a convolutional Siamese network with a ResNet-101 [25] architecture that had been pre-trained on the ImageNet dataset. To accommodate the specific requirements of their algorithms for the retina and knee, the final fully connected layer of ResNet-101 was modified to output three or five nodes, respectively, from each sub-network.The implementation of [PERSON] et al. was conducted in Python, utilizing the Adam optimizer with a learning rate of \(5\times 10^{-7}\). During training and validation, a batch size of 16 was employed, and the model was saved based on the lowest validation loss for subsequent testing evaluation. The results obtained from their experiments indicate that the utilization of Siamese Neural Networks and Euclidean distance measurements enables the representation of disease severity on a more nuanced and continuous spectrum compared to traditional categorical disease classification systems. In a recent study by [PERSON] et al. [26], the authors propose the use of Siamese Neural Networks (SNNs) for the continuous evaluation of eye disease severity. Similar to the work of [PERSON] et al. [24] mentioned earlier, they employ a twin-CNN architecture. The proposed system is specifically demonstrated in the domain of diabetic retinopathy. However, it is important to note that [PERSON] et al. utilize a Siamese Triplet network, which aims to determine the distance between image embeddings. For the sub-network implementation, ResNet-101, a convolutional neural network architecture, was used. They incorporate a triplet mining algorithm, where the data are provided as triplet image pairs comprising an anchor image, a positive image, and a negative image. Each image pair is passed through the pre-trained network, which learns the distributed embedding of the images based on their similarities and dissimilarities with respect to the anchor image. The Euclidean distance between the images is then computed from the final connected layer, representing the difference between the images. This distance serves as an abstraction of the severity score for the respective image. In their study, [PERSON] et al. [27] proposed the use of Siamese Convolutional Neural Networks (CNNs) for the assessment of the continuous spectrum of lung edema severity using chest radiographs. Unlike the previous works mentioned, they employed a pre-trained CNN architecture called DenseNet121 [28] instead of ResNet-101 [25]. The authors utilized the Euclidean distance as a measure of similarity between images. For the optimization process, the Adam optimizer was chosen for all models, with a learning rate of 2e-5. Model weights were saved at each epoch if the validation loss decreased. If the validation loss plateaued or did not improve for more than 10 epochs, early stopping was applied. The results showed that their model successfully assessed the severity of pulmonary edema from chest radiography. To label the dataset used in this research, two certified radiologists participated in the study.