aceAn Agentic AI Workflow for Automated Macular Hole Diagnosis and Structured Report Generation
Introduction: Current AI models for optical coherence tomography (OCT) primarily focus on diagnosis as a standalone task. We developed and validated an end-to-end agentic AI workflow that integrates automated macular hole (MH) diagnosis from OCT images with structured patient-friendly report generation. Methods: The proposed sequential workflow coordinated by a workflow orchestration agent consisted of an image diagnosis stage and a language generation step. The OCT version of RETFound, a self-supervised retinal…
A fine-tuned retinal foundation model detected macular holes on external OCT datasets with high accuracy, while a language model produced standardized patient-friendly reports without influencing the diagnosis.
The task was a binary classification of macular hole versus normal macula using public OCT datasets, so the study does not show how the workflow performs on other retinal conditions or in live clinical use.
Evidence
- Peer-reviewedOphthalmology and Therapy2026-10-23
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Truvace Impact Record TRV-2026-2232, v1: “An Agentic AI Workflow for Automated Macular Hole Diagnosis and Structured Report Generation.” Truvace, 2026-10-24. /record/TRV-2026-2232 (accessed at citation time). sha256 64e04cf50acf9988…
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