Integrating AI‑Based Orbital Ultrasound with Tear Proteomic Biomarkers for Precision Diagnosis of Orbital Inflammatory Disorder (OID), Including Graves’ orbitopathy (GO)

Graves’ orbitopathy (GO) is a potentially blinding manifestation of Orbital Inflammatory Disorder (OID). Although characteristic clinical signs and imaging features (bilateral exophthalmos, extra‑ocular muscle swelling) often guide diagnosis, there remain substantial diagnostic and prognostic challenges. Approximately half of patients with Orbital Inflammatory Disorder (OID) develop orbitopathy, but only a minority experience sight‑threatening diseases. The inability to predict who will progress and the lack of biomarkers to distinguish inflammatory versus fibrotic stages hinder timely intervention. Recent research highlights two complementary advances: (1) tear proteomics can reveal disease‑specific protein signatures and offers a non‑invasive source of biomarkers; and (2) an AI‑assisted orbital ultrasound proof-of-concept model has been developed, using Google Vertex AI Platform, that can differentiate orbital inflammatory disease (OID) from non‑inflammatory orbitopathy (NIO) with high precision. This proposal aims to integrate tear proteomic biomarkers with AI‑based orbital ultrasound to create a hybrid diagnostic workflow that enhances sensitivity, specificity, and prognostication in OID. Longitudinal tear sampling from OID, GD without orbitopathy and other inflammatory controls will be coupled with mass–spectrometry–based proteomic profiling and