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

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Deep learning for intelligent diagnosis in thyroid scintigraphy.

Tingting Qiao¹, Simin Liu¹, Zhijun Cui²

¹Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.

The Journal of International Medical Research

|January 15, 2021

Summary

This summary is machine-generated.

Deep learning models show high accuracy in diagnosing thyroid diseases via scintigraphy. These AI tools assist nuclear medicine residents, significantly speeding up diagnostic times for conditions like Graves' disease.

Keywords:

Graves’ disease Intelligent diagnosis deep learning diagnostic performance nuclear medicine residents subacute thyroiditis thyroid disease thyroid scintigraphy

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Area of Science:

Medical Imaging
Artificial Intelligence
Endocrinology

Background:

Thyroid scintigraphy is crucial for diagnosing thyroid diseases.
Current diagnostic processes can be time-consuming and require expert interpretation.

Purpose of the Study:

To develop deep learning (DL) models for enhanced accuracy and efficiency in thyroid disease diagnosis using scintigraphy.
To evaluate the performance of different DL architectures (AlexNet, VGGNet, ResNet).

Main Methods:

DL models were constructed using AlexNet, VGGNet, and ResNet architectures with transfer learning.
Model performance was assessed using recall, precision, NPV, specificity, accuracy, and F1-score.
Diagnostic performance and speed were compared between DL models and nuclear medicine residents.

Main Results:

DL models achieved high performance metrics, with scores ranging from 73.33% to 97.00%.
All models demonstrated strong inter-rater reliability (Kappa > 0.710).
Models were significantly faster (400-600x) than residents and aided diagnostic accuracy, particularly the ResNet model.

Conclusions:

Deep learning models are effective tools for the diagnostic assessment of thyroid scintigraphy.
These AI models can assist nuclear medicine residents in diagnosing Graves' disease and subacute thyroiditis.
DL integration promises to improve efficiency and potentially accuracy in thyroid disease diagnostics.