The increasing prevalence of autoimmune thyroid diseases and thyroid cancer highlights the urgent need for improved diagnostic support approaches. Traditional diagnostic methods often rely primarily on biochemical markers or qualitative imaging evaluations, which may delay accurate disease identification and hinder timely treatment. The present study demonstrates that machine learning models integrating biochemical, demographic, and ultrasound data achieve strong classification performance for thyroid disorder identification. Tree-based algorithms, such as XGBoost and Random Forest, demonstrated strong performance, while deep learning models achieved high accuracy in imaging-based classification tasks. Although the results highlight the potential of multi-source data-driven approaches to support clinical decision-making, performance variability indicates the need for validation on larger and more diverse datasets. Future work should focus on expanding data sources, incorporating additional biomarkers, and improving model interpretability to facilitate clinical translation.
Andreou et al. (Tue,) studied this question.