A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' clinical experience and professional expertise, and are further limited by factors such as image resolution and inter-patient variability. These limitations hinder the accuracy and efficiency of clinical diagnosis. Leveraging its powerful image processing capabilities, deep learning has been widely applied in the extraction of nodule features and the preliminary classification of benign and malignant cases, bringing transformative advances to medical image analysis. In this review, we systematically summarize recent developments in the diagnosis of thyroid nodules using deep learning from three key perspectives: model architectures, training methods, and core tasks in thyroid nodule medical image analysis. We compare the various architectures, including CNNs, RNNs, GANs, transformers and hybrid models. We then summarize key challenges in thyroid nodule imaging, outline potential solutions, and consider how deep learning can be integrated into clinical workflows. Looking ahead, we discuss the future directions of enhancing the applicability of deep learning from model robustness, cross-domain adaptation, and clinical interpretability. Our work aims to provide valuable reference insights and directions for improvement for future related research.