Evaluating the efficacy of the ResNet50 deep learning model utilizing thyroid scintigraphy images for predicting the outcomes of initial iodine-131 therapy in patients with Graves’ disease

This study introduces a pioneering deep learning framework that employs thyroid scintigraphy radiomics to predict iodine-131 therapeutic outcomes in GD. By combining ResNet50 for feature extraction with interpretable Grad-CAM localization, this approach advances personalized nuclear medicine strategies and mitigates the 'black box' limitation typically associated with artificial intelligence models. These findings require validation across multicenter cohorts to refine and optimize precision treatment protocols.