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Updated: Sep 3, 2025
04:23
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
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Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in
Hao-Chih Tai1, Kuen-Yuan Chen1, Ming-Hsun Wu1
1Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan.
Biomedicines
|July 27, 2022
Summary
Computer-assisted detection (CAD) software accurately quantifies thyroid nodule sonographic features. This AI tool significantly improves clinician diagnostic performance, enhancing thyroid ultrasound interpretation.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Thyroidology
Background:
- Thyroid nodule management relies on sonographic feature analysis.
- Advancements in AI/ML have led to computer-assisted detection (CAD) software for thyroid nodules.
Purpose of the Study:
- Validate the accuracy of computerized sonographic features (CSF) from AmCAD-UT software.
- Assess the impact of CSF on clinician reading performance for thyroid nodules.
Main Methods:
- Feature detection accuracy tested against expert-defined ground truth.
- Multiple-reader multiple-case (MRMC) study evaluated sequential reading performance with and without CSF.
- Five specific sonographic features were analyzed.
Main Results:
- Computerized features demonstrated high accuracy (AUCs 0.627–0.946).
- Clinician performance significantly improved with CSF assistance (AUC increased from 0.720 to 0.776).
Conclusions:
- Computerized sonographic features are accurate and consistent with clinical findings.
- AmCAD-UT software provides valuable assistance for sonographic diagnosis of thyroid nodules.
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