## 4 Discussion
While traditional diagnosis relies on clinician-evaluated biomarkers such as the optic disc, cup, and rim, our model has the advantage of detecting objective biomarkers related to glaucoma progression, particularly in vasculature. Because the model was only input CFPs and their corresponding labels, the highlighted areas are featuresthat the model learned autonomously. These findings suggest that AI-based models can potentially complement existing diagnostic methods by adding to the clinical endpoints and biomarkers that help detect glaucoma and monitor its progression more effectively. Our preliminary investigation as demonstrated by the Grad-CAM results in some neural network layers indicates that all models predominantly focus on vasculature features, regardless of whether glaucoma impacts visual structures or functions. Such an association between vascular structures and glaucoma was also found in a recent vasculature imaging modality: optical coherence tomography angiography (OCTA) [55, 56, 57, 58, 59, 60, 61, 62, 63, 64]. This biological marker is logical; retinal ganglion cells require blood oxygen to function and transmit a visual input to the brain [65]. The vascular theory posits that reduced ocular blood flow (OBF) is \"pathogenetically relevant\" to glaucoma progression [66]. Reduced OBF induces ocular damage, leading to the destruction of the optic nerve and apoptosis of retinal ganglion cells, symptoms associated with glaucoma. Several studies have shown this link [65, 67, 68, 69, 70, 71, 72], though this theory remains controversial [65, 71, 73]. As shown in the A panels of Figure 3, our use of artificial intelligence may provide an objective backing to the role of reduced OBF in glaucoma progression. In some neural network layers, our model identified early manifestations of glaucoma in the vasculature and focused on vasculature across all longitudinal images in making its prediction. As with all studies, our study also has several limitations. One issue was the automatic cropping of raw fundus stereophotographs with a Python algorithm we developed that automatically removed black borders around the images. In some cases, the algorithm inadvertently cropped out significant portions of the fundus photo (Figure 4). Additionally, the quality of the images varied significantly due to the different types of fundus cameras used across the 22 study sites, which could have hindered the performance of our models, as they had to adapt to a wide range of visual inputs. However, this may have also produced a more generalizable model, able to detect POAG progression even from poorer-quality photos. We plan several avenues for future work. First, we plan to analyze and compare Grad-CAMs generated by different layers in our CNN model; we only analyzed two layers, and the model contains over 100 different layers. The analysis of these different layers may provide further insights into potential POAG biomarkers. Second, we plan to utilize different model architectures, specifically transformer models, for longitudinal prediction in parallel instead of the \"sequential\" prediction of LSTM. [PERSON] et al. found that one of their developed transformer models achieved an AUC of 0.91 in glaucoma detection on the OHTS dataset [48]; however, their study only utilized fundus photos from patients' baseline visits, while we plan to use longitudinal data for glaucoma progression detection. Third, we also plan to implement localized VF data into our model to improve  
Figure 4: Side-by-side comparison of a color fundus photo pre- and post-mis-cropping by the cropping algorithm developed, which automatically determines areas with black borders. Of note, these cases were infrequent. glaucoma progression detection. Fourth, as described in the Introduction, there are several attention techniques--trainable and non-trainable.