ArticleQuantitative imaging in medicine and surgery2025
Dual-stage artificial intelligence-powered screening for accurate classification of thyroid nodules: enhancing fine needle aspiration biopsy precision.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cardiac echocardiographic analysis with multi-scale effective fusion module: a novel stroke prediction approach.BMC medical imaging · 2026Article
- Artificial intelligence and elastography in diagnostic work-up of thyroid nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: Accurately differentiating thyroid conditions, particularly malignant and benign nodules, is crucial for effective treatment planning. Fine needle aspiration biopsy (FNAB), the standard diagnostic method for suspected malignancies detected by ultrasound, is invasive and can be unnecessary. This study introduces a novel dual-stage deep learning architecture for four-class classification of thyroid nodules, aiming to improve diagnostic accuracy and reduce unnecessary procedures. The framework integrates segmentation and classification stages, offering more precise and consistent analyses than traditional binary classification, potentially improving guidance for FNAB decisions and reducing invasiveness. Methods: Our dual-stage deep learning framework integrates segmentation and classification. The segmentation stage uses K-Net to delineate nodule boundaries. The classification stage employs MobileViT to classify nodules into four categories: papillary carcinoma, medullary carcinoma, nodular goiter with adenomatous hyperplasia, and chronic lymphocytic thyroiditis (CLT). Results: K-Net achieved a mean intersection over union of 87.06% in segmenting the nodule boundaries. MobileViT achieved a mean accuracy of 92.33% on the validation set and 90.27% on the test set. The confusion matrix demonstrated high diagonal values, indicating accurate classification across all categories. The model demonstrated a precision of 91.30% and recall of 89.76% on the validation set. Performance was particularly strong for papillary thyroid carcinoma and CLT. Conclusions: This approach offers an efficient tool for analyzing thyroid nodules, potentially improving guidance for FNAB decisions and reducing invasiveness. The framework shows promise for clinical application, offering a non-invasive method to aid in the differential diagnosis of thyroid nodules. By integrating advanced segmentation and classification, the model offers a refined approach to thyroid nodule analysis.
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