[
    {
        "id": "osp-25073",
        "type": "article-journal",
        "title": "Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors",
        "author": [
            {
                "family": "Rahman",
                "given": "Taiabur"
            },
            {
                "family": "Rahman",
                "given": "Siddiqur"
            },
            {
                "family": "Moniruzzaman",
                "given": "Muhammad"
            },
            {
                "family": "Kawsar",
                "given": "Ummay"
            },
            {
                "family": "Ratna",
                "given": "Sayedatunnessa"
            },
            {
                "family": "Siddique",
                "given": "Shadman"
            },
            {
                "family": "Siddique",
                "given": "Rafsan"
            },
            {
                "family": "Ahmad",
                "given": "Tausif"
            },
            {
                "family": "Ahmad",
                "given": "Tahsin"
            },
            {
                "family": "Rabbani",
                "given": "Golam"
            }
        ],
        "URL": "https://omanscience.com/en/articles/deep-learning-of-longitudinal-visual-fields-predicts-glaucoma-progression-rate-and-identifies-fast-progressors",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Glaucoma is the leading cause of irreversible blindness, and timely identification of fast progressors is essential to prevent disability. Current practice estimates progression by ordinary least-squares regression of mean deviation (MD) on time, requiring 6--10 visual field (VF) tests over several years to obtain a reliable slope. We present GLAM (Glaucoma Longitudinal Analysis Model), a deep learning framework that ingests longitudinal Humphrey 24-2 total deviation sequences with five clinical features and predicts MD and visual field index progression rates using attention-based fusion and aleatoric uncertainty. On the open-access University of Washington Humphrey Visual Field dataset (4,276 patient-eyes), GLAM achieved an MD-rate mean absolute error of 0.139 dB yr$^{-1}$ ($R^2 = 0.927$; 73.5% reduction over a ridge baseline) and an AUC of 0.990 for fast-progressor detection. VF-only deep learning can match multimodal pipelines for progression prognostication using routinely collected perimetry alone."
    }
]