This retrospective study aimed to develop deep-learning radiomics models based on optical coherence tomography (OCT) scans to evaluate the activity of thyroid eye disease. The study included 33 patients (66 orbits) diagnosed with thyroid eye disease at Beijing Tongren Hospital between July 2021 and August 2022. We collected OCT scans, clinical activity score, and medical records of the patients. Patients were divided into active and inactive groups based on the clinical activity score, which were then divided into a training set and a test set at a ratio of ∼7:3. The macula-centered horizontal meridian image was used for the identification of the regions of interest using 3D slicer. Radiomics features were extracted and selected by t test and least absolute shrinkage and selection operator regression algorithm with 10-fold cross-validation. The random forest (RF) model and support vector machine (SVM) model were built based on retinal or choroid features and validated by receiver operating characteristic curves and area under the curve (AUC). For the retinal features, AUC were 0.800 (RF) and 0.840 (SVM) in the test set, and for the choroid features, the AUC were 0.733 and 0.813, for the RF model and SVM model, respectively. For the confusion matrix, the choroid-based SVM model had more balanced parameters compared with the retina-based SVM model. OCT-based deep learning radiomics analysis can be used to evaluate activity, which provide convenience in clinical practice.Copyright © 2024 by Mutaz B. Habal, MD.