ArticleNPJ digital medicine2025
Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Glaucoma is a globally prevalent disease that leads irreversible blindness. The visual field (VF) examination is important but time-consuming for visual function evaluation with high requirement of cooperation and reliability of patients. While color fundus photographs (CFPs) are easy to access. Here, we proposed a multi-modal longitudinal estimation deep learning (MLEDL) system, capable of predicting present and future VFs from CFPs and clinical text. This model was developed on 1598 records in cross-sectional and 3278 records in longitudinal dataset, with 446 external testing records. The pointwise mean absolute error across five models ranged from 3.098 to 4.131 dB. Heatmaps demonstrated the spatial relationship between fundus damage and vision loss. VF grading methods were employed for verifying the clinical reliability. Consequently, our MLEDL facilitates VF prediction by CFPs and clinical narratives, offering potential as function assessment tool over the long-duration course of glaucoma and thereby improving clinical practice efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.