aceDeep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging
Purpose This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clinical activity score and key patient characteristics in thyroid eye disease. Methods The publicly available TOM500 dataset, consisting of orbital magnetic resonance imaging scans and clinical data from 500 thyroid eye disease patients, was split into training ( n = 360), validation ( n = 100), and test ( n = 40) sets. A ResNet-50 convolutional neural network pretrain…
Deep learning models using orbital MRI predicted continuous clinical activity score with low error and inferred patient characteristics including age, sex, and smoking status.
Evidence
- Peer-reviewedOrbit2026-07-28
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Truvace Impact Record TRV-2026-0592, v1: “Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging.” Truvace, 2026-07-30. /record/TRV-2026-0592 (accessed at citation time). sha256 1e9b129c08bbd6b0…
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