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Related Experiment Video

Updated: Jul 18, 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

Leveraging deep learning to identify calcification and colloid in thyroid nodules.

Chen Chen^1,2,3,4, Yuanzhen Liu^2,3,4, Jincao Yao^2,5,6

¹Graduate School, Wannan Medical College, Wuhu, 241002, China.

|August 28, 2023

Summary

Deep learning (DL) models significantly outperform radiologists in differentiating thyroid nodule calcifications from colloid on ultrasound images. This AI advancement improves diagnostic accuracy for echogenic foci.

Keywords:

Calcification Colloid Deep learning Thyroid nodule Ultrasound

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Published on: June 2, 2022

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Thyroid nodules present echogenic foci on ultrasound, which can represent calcification or colloid.
Distinguishing between calcification and colloid is crucial due to their differing malignancy risks.

Purpose of the Study:

To evaluate the performance of a deep learning (DL) model in differentiating calcification from colloid in thyroid nodules.
To compare the diagnostic accuracy of the DL model against human radiologists.

Main Methods:

A retrospective study utilized 30,388 ultrasound images from 1127 pathologically confirmed thyroid nodules.
The YoloV5 transfer learning model was trained and tested for distinguishing echogenic foci.
Performance was evaluated using sensitivity, specificity, and accuracy, with the area under the receiver-operator characteristic curve (AUC) as the primary index.

Main Results:

The DL model achieved higher average sensitivity (78.41%), specificity (91.36%), and accuracy (77.81%) in test group 1 compared to radiologists (51.14%, 82.58%, 61.29%).
For smaller echogenic foci in test group 2, the DL model also demonstrated superior performance (70.17% sensitivity, 77.14% specificity, 73.33% accuracy) versus radiologists (57.69%, 63.29%, 59.38%).

Conclusions:

Deep learning models show superior capability in differentiating thyroid nodule echogenic foci as calcification or colloid.
The study highlights the potential of AI in enhancing diagnostic precision in thyroid nodule evaluation.