## 4 Discussion  
Our study demonstrates that the CNN-LSTM models are highly effective in detecting the progression of POAG using longitudinal CFPs. Our model that integrated both structural and functional clinical endpoints significantly outperformed the other models that relied solely on either structural or functional data. When analyzing the generated heatmaps of the features the models most focused on when making their predictions, all models favored vascular structures in the earlier layers of the feature extractor and disc regions by the final layer. Our results underscore the challenge of detecting POAG progression based on optic disc changes alone, as the \"Disc\" model achieved a lower AUC than the \"VF\" model, which relied on visual field changes. This difference aligns with clinical observations that disc changes are more subtle and harder to recognize than VF changes, which are more readily detectable when tests are reliable. Our study is unique in its application of a longitudinal dataset, setting it apart from previous research using the OHTS dataset [35, 48]. Unlike these studies that focused on static images, our model leverages the dynamic information across different longitudinal visits provided by sequential CFPs, offering a more nuanced understanding of POAG progression. The \"Both\" model outperformed the other models in terms of AUC, precision, recall, and F1-score in detecting POAG progression. This advantage is likely due to the occurrence of both structural and functional features/biomarkers associated with the progression of POAG in image training data, allowing the model to detect a more comprehensive set of indicators for POAG development. In contrast, the \"Disc\" model, which only had structural changes but preserved visual function, and the \"VF\" model, which only focused on functional changes, achieved lower AUCs. Our findings, that incorporating both structural and functional changes in glaucoma improves model performance, support the literature. [PERSON] et al. found that their neural network performed significantly better in detecting glaucoma progression when trained on fused structural and functional glaucoma changes compared to when trained only on functional changes [49]. [PERSON] et al. found that combining functional and structural information significantly improves model performance in detecting glaucoma compared to only using structural information [50]. Several other publications exist investigating the improvement in AI models when combining structural and functional inputs in glaucoma detection tasks [51, 52, 53, 54]. However, these publications only use baseline patient visits and thus fail to account for the long-term progression of glaucoma and dynamic longitudinal information found in subsequent follow-up images. A key benefit of utilizing longitudinal CFPs is the potential to uncover early biomarkers of POAG, leading to earlier and more accurate diagnoses in clinical settings. By integrating dynamic information from sequential imaging, AI-based models can detect subtle changes over time, which might be missed in single snapshots [9]. This capability is particularly valuable in addressing the growing shortage of ophthalmologists in the U.S. and globally [11, 12, 13]. These tools can also help standardize care across diverse populations, ultimately improving access to quality eye care and mitigating disparities in glaucoma diagnosis and treatment [10, 27, 28, 29]. By analyzing changes over time, our CNN-LSTM architecture, which leverages longitudinal data, demonstrated a superior ability to capture the dynamic progression of POAG compared to static image models that are trained on single patient visits from the OHTS dataset [35, 48]. The use of explainability techniques, such as Grad-CAM, provides insights into the model's decision-making process, highlighting areas on the image of important relevance when making predictions.