arXiv:2508. 03750v2 Announce Type: replace Abstract: Early and accurate glaucoma detection is critical to prevent irreversible vision loss, yet existing AI methods often rely on unimodal inputs and lack interpretability.
arXiv:2607. 04673v1 Announce Type: cross Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability.
arXiv:2609.06699v1 Announce Type: cross Abstract: Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent perma...
arXiv:2607. 25324v1 Announce Type: cross Abstract: Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure.
OptiModNet is a lightweight UNet‑Transformer hybrid designed for optic disc and cup segmentation. It incorporates grouped‑query and channel attention across multiple stages, along with an Aggregated Pyramid Loss to improve gradient flow and structural consistency. Evaluated on the REFUGE2 dataset, it surpasses existing methods by over 2.5 % while using only 3.73 GFLOPs and 1.93 M parameters.
Deep learning framework GLAM predicts glaucoma progression rates from longitudinal Humphrey 24‑2 visual field data and five clinical features, achieving a mean absolute error of 0.139 dB yr⁻¹ and an AUC of 0.990 for fast‑progressor detection. Using attention‑based fusion and aleatoric uncertainty, GLAM outperforms a ridge regression baseline by 73.5% in MD‑rate prediction. The model demonstrates that visual field data alone can match multimodal pipelines for progression prognostication.
arXiv:2608. 10316v1 Announce Type: cross Abstract: Multi-modal learning combining medical images and clinical text is promising for disease diagnosis.
arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.
The study developed a Vision Transformer-based deep learning model with uncertainty estimation to detect glaucoma from colour fundus photographs across multi‑ethnic populations, including those with high myopia. Using 56,483 images for training, the model achieved an internal AUROC of 98.7% and maintained high performance (AUROC 86.4–99.6%) on 16 external datasets from eight countries. In high‑myopia eyes, the model outperformed ophthalmologists and matched specialists when full clinical data were available.
Background: Artificial intelligence (AI)-based glaucoma detection from colour fundus photographs (CFP) offers scalable screening, but performance may decline on external datasets because of difference...
arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
arXiv:2608.24723v1 Announce Type: new Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent re...