Interpretable machine learning for severity classification of thyroid eye disease using orbital anatomical features

Background Thyroid eye disease (TED) severity assessment using the EUGOGO severity classification is partly subjective and prone to interobserver variability. While MRI-derived anatomical measurements offer objective features, such as ocular protrusion and extraocular muscle thickness, these are underutilized in machine learning (ML) models that often rely on non-interpretable radiomic features. Moreover, the inclusion of longitudinal scans from the same patient may artificially inflate model pe