aceArtificial intelligence for ophthalmic vessel segmentation: from multimodal imaging to clinical translation
Artificial intelligence (AI)-based vessel segmentation converts color fundus photographs, scanning laser ophthalmoscopy (SLO), optical coherence tomography angiography (OCTA), structural OCT, and anterior-segment images into quantitative vascular biomarkers. Strong performance within individual datasets has not consistently yielded reliable clinical measurements because errors can arise during acquisition, anatomical preprocessing, vessel extraction, biomarker calculation, and deployment. This structured narrati…
AI vessel segmentation that performs well within single datasets has not consistently produced reliable clinical measurements, because errors can enter at every step from image acquisition to deployment.
Studies are not directly comparable because devices, data splits, labels and post-processing differ substantially, so the review summarizes metrics without ranking models.
Evidence
- Peer-reviewedFrontiers in Medicine2026-10-19
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Truvace Impact Record TRV-2026-2039, v1: “Artificial intelligence for ophthalmic vessel segmentation: from multimodal imaging to clinical translation.” Truvace, 2026-10-20. /record/TRV-2026-2039 (accessed at citation time). sha256 10edc3af67a931d9…
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