The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
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.
The study presents an interpretable AI framework for glaucoma screening using low-cost handheld retinal cameras in a West African population. Trained on 681 participants, the system achieved high performance across vessel segmentation, cup/disc segmentation, and optic nerve head feature detection, with classification AUCs of 0.85 for the handheld device and 0.93 for a tabletop camera. The model provides confidence scores and visual explanations to aid clinical interpretation.
arXiv:2608. 07651v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical image interpretation but suffer from hallucination, limited accuracy, and run-to-run inconsistency.
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: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.
The paper introduces GlobeReady, a clinician-friendly platform that leverages the RetiGlobe foundation model for ophthalmic image diagnostics without requiring fine-tuning. RetiGlobe was pretrained in two stages: first with self-supervised learning on 38 million synthetic images, then with contrastive learning on 475,845 real image‑text pairs from diverse ethnicities, devices, and regions. GlobeReady was evaluated on 488,448 images from multiple international centers and tested prospectively with 31 ophthalmologists, also exploring domain generalisability, uncertainty quantification, OOD detection, and feature-based case retrieval.