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Updated: Aug 29, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Artificial intelligence defines protein-based classification of thyroid nodules.

Yaoting Sun^1,2,3, Sathiyamoorthy Selvarajan⁴, Zelin Zang⁵

¹Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.

|September 6, 2022

Summary

This study introduces an AI-powered protein biomarker panel to accurately classify thyroid nodules. This novel approach enhances diagnostic precision for malignancy using both FFPE and FNA tissues.

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Area of Science:

Biomarker Discovery
Artificial Intelligence in Medicine
Proteomics

Background:

Accurate diagnosis of thyroid nodules is challenging.
Existing methods face limitations with small tissue samples.

Purpose of the Study:

To develop and validate an AI-driven protein biomarker panel for thyroid nodule classification.
To assess the clinical utility of this panel for both FFPE and fine-needle aspiration (FNA) specimens.

Main Methods:

Developed a neural network model using proteomic data from 1724 FFPE thyroid tissue samples.
Validated the classifier on retrospective FFPE and prospective FNA biopsy cohorts from multiple clinical centers.

Main Results:

The AI classifier achieved >91% accuracy in the discovery set for malignant thyroid nodules.
External validation showed 89% accuracy for FFPE and 85% accuracy for FNA biopsies.
The panel demonstrated feasibility and utility across diverse clinical settings.

Conclusions:

AI-integrated high-throughput proteomics offers a precise diagnostic solution for challenging thyroid nodule cases.
This protein-based biomarker panel shows significant potential for improving thyroid cancer diagnosis.
The method is adaptable for both FFPE and limited FNA tissue amounts.