## 1 Introduction  
Glaucoma, a group of optic neuropathies, is the leading cause of irreversible blindness around the world, accounting for over 14% of the total blind population, and predicted to affect over 110 million people by 2040 [1,2]. Primary open-angle glaucoma (POAG) is the most common form of glaucoma, characterized by commonly known progressive damage to the optic nerve head (ONH) and retinal nerve fiber layer (RNFL), resulting in the gradual loss of peripheral vision or visual field (VF), and then the loss of central vision [3]. Ocular hypertension (OHT) and POAG are interconnected conditions, with OHT often considered a precursor or early stage of POAG [4,5]. In 1994, the Department of Ophthalmology and Visual Sciences at Washington University designed the prospective, multicenter clinical trial named the Ocular Hypertension Treatment Study (OHTS) to evaluate the efficacy of 20% intraocular pressure reduction using ocular medications in delaying visual function loss and ONH damage due to POAG onset from OHT [6]. The primary endpoint of the OHTS was the development of POAG, marked by clinically defined structural changes in the optic disc at the ONH or functional changes in VF, or the presence of both structural and functional changes [7]. These changes were initially manually assessed by the Optic Disc Reading Center (ODRC) and Visual Field Reading Center (VFRC), respectively. The ODRC and VFRC consisted of a panel of masked reviewers who monitored optic disc and visual field abnormalities. If two consecutive CFPs or three consecutive VF exams were determined to have changes from the baseline tests, they were subsequently sent to the Endpoint Committee, a panel of three glaucoma specialists who reviewed a patient's ONH photographs or VFs to determine if the changes were attributable to POAG or other diseases. The manual assessment of POAG progression is time-consuming, costly, and prone to human error [8,9]. The U.S. healthcare system spends around USD 2.5 billion annually for glaucoma treatment costs [10]. There also exists an immense shortage of ophthalmologists, and major workforce shortages are predicted worldwide in the upcoming decades [11,12,13], which affects positive visual health outcomes [14,15,16,17,18]. Additionally, significant disparities exist in healthcare access and outcomes for glaucoma, particularly among racial and ethnic minorities. Black, Asian, and Hispanic Americans face a higher incidence of glaucoma compared to non-Hispanic White Americans [19,20,21,22] and encounter barriers to timely diagnosis and treatment [23,24,25,26,27]. Socioeconomic inequalities further impact access to quality care [10,28,29], leading to worse outcomes for these populations [30,31]. Thus, there is a potential avenue for artificial intelligence (AI) to automatically detect POAG-related changes to keep up with the increasingly rapid demand of glaucoma detection and management and help address this unmet need in underserved populations. Numerous studies have explored AI-based deep learning models for automated glaucoma classification tasks. For example, [PERSON] et al. utilized five convolutional neural networks (CNNs) and a classifier fusion to detect early cases of glaucoma [32]. [PERSON] et al. achieved approximately 96.9% accuracy in predicting glaucoma using a U-Net model [33]. [PERSON] et al. reported a 0.976 area under the receiver operating characteristic curve (AUC) for their generalizable glaucoma classification model [34]. [PERSON] et al., using a CNN on the OHTS dataset, achieved an area under curve (AUC) of 0.880 for glaucoma classification on fundus photos of patients with optic disc or VF changes [35]. While there are extensive studies in the literature exploring AI-based models for POAG classification, several limitations exist. These models frequently only look at one fundus image per patient at a time, thus failing to account for the long-term development or progression of glaucoma from subsequent follow-up images.