Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules

Purpose To develop a digitized integrated feature-based interpretable machine learning classification model to accurately recognize complex thyroid nodules while efficiently diagnosing conventional thyroid nodules (thyroid nodules with typical benign or malignant US features). Materials and Methods Thyroid US images depicting pathologically confirmed nodules were retrospectively collected from seven medical centers in China (January 2011-December 2021). An interpretable classification model consisting of two independent masks was developed and defined as "UltraMC." The front-end network was trained to identify conventional thyroid nodules using four digitized features, and the back-end network collected nodules classified as benign in the previous framework for secondary analysis to clarify their final diagnosis. UltraMC performance was evaluated using accuracy, sensitivity, specificity, and confusion matrices. Results The total dataset included 73 826 patients with thyroid US images (mean age, 45.56 years ± 11.21 [SD]; 54 398 female). Diagnostic accuracy of the front-end network for detecting conventional thyroid nodules was 92.9% (13 718 of 14 765), and accuracy of the back-end network for classifying mummified thyroid nodules (MTNs) was 88.5% (652 of 737). The overall diagnostic accuracy of the US MTN classification model (UltraMC) was 91.8% (14 228 of 15 502). The areas under the receiver operating characteristic curve of the front-end network and UltraMC in identifying conventional thyroid nodules were 0.98 (95% CI: 0.98, 0.98) and 0.96 (95% CI: 0.96, 0.97), respectively. Conclusion The proposed two-layer interpretable classification model achieved high diagnostic accuracy for both conventional and mummified thyroid nodules. These findings demonstrate that digitized US features integrated into a white box framework can effectively support classification of complex thyroid nodules. <b>Keywords:</b> Ultrasound, Head/Neck, Thyroid, Diagnosis, Convolutional Neural Network (CNN), K-Means, Random Forest, Thyroid Nodule, Interpretable, Digital, Mummified Thyroid Nodules <i>Supplemental material is available for this article.</i> © RSNA, 2026.