Artificial Intelligence–Driven Early Detection and Management of Thyroid Eye Disease: A Multimodal Diagnostic Approach

Abstract

Thyroid Eye Disease (TED) which doctors also call Graves' Orbitopathy constitutes an intricate autoimmune condition that causes inflammation to the orbital area and typically occurs with thyroid gland disorders. Vision loss becomes permanent when doctors do not diagnose patients early enough and provide timely treatment. The existing diagnostic procedures depend primarily on the assessment skills of medical professionals together with their capability to analyze medical images and their use of clinical judgment which creates differences in patient diagnosis and treatment schedule. This paper explores the integration of Artificial Intelligence (AI) techniques in the detection, classification, and management of Thyroid Eye Disease. The proposed approach employs machine learning together with deep learning techniques to process multiple data types which include orbital imaging (CT/MRI), clinical photographs, and patient health records. AI-based systems can assist in identifying early signs of TED, grading disease severity, and predicting disease progression with higher accuracy and consistency. The research shows that artificial intelligence helps doctors create personalized treatment plans by using predictive analysis to find the best treatment options.