Table 1 A summary of studies utilizing multimodal AI approaches in glaucoma
Year | Author | Task | Multimodal data types | Dataset scale | Dataset availability |
|---|---|---|---|---|---|
2021 | Mehta P et al. [34] | Glaucoma detection | Fundus images, OCT images | 1,283 eyes of 771 glaucoma patients | Upon request |
2022 | Xiong J et al. [36] | Glaucomatous optic neuropathy | Visual field reports, peripapillary circular OCT scans | 2,463 pairs of VF and OCT images from 1083 patients | Private dataset |
2023 | Huang X et al. [37] | Glaucoma management | Visual field, fundus images, intraocular pressure, OCT images | 1,115 follow-up records of 263 eyes | Freely available in https://springernature.figshare.com/collections/GRAPE_A_multimodal_glaucoma_dataset_of_followup_visual_field_and_fundus_images_for_glaucoma_management/6406319/1 |
2021 | Wu J et al. [38] | Glaucoma grading | Fundus images, OCT images | 300 patients | Free available after registration in https://gamma.grandchallenge.org/ |
2023 | Wu J et al. [39] | Glaucoma grading | Fundus images, OCT images | 300 patients | Free available after registration in https://gamma.grand-challenge.org/ |
2023 | Zhou Y et al. [40] | Glaucoma recognition | Fundus images, OCT images | 1,200 images | Free available after registration in https://ichallenges.grand-challenge.org/iChallenge-PM/ |
2023 | Luo Y et al. [43] | Glaucoma detection and progression forecasting | OCT images of glaucoma detection and progression | 1,000 samples from 1,000 patients | Free download after approval |