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Related Experiment Video

Updated: Sep 13, 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

Optimizing Thyroid Nodule Management With Artificial Intelligence: Multicenter Retrospective Study on Reducing

Jia-Hui Ni¹, Yun-Yun Liu¹, Chao Chen²

¹Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, YanChang Middle Street 301, Shanghai, China, 86 21-66307539.

JMIR Medical Informatics

|July 30, 2025

Summary

Artificial intelligence (AI) effectively identifies benign thyroid nodules, significantly reducing unnecessary fine needle aspirations (FNA). While AI shows promise as a diagnostic tool, careful surveillance remains crucial for potentially missed malignant nodules.

Keywords:

artificial intelligence fine needle aspiration risk stratification thyroid nodules ultrasound

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Current artificial intelligence (AI) models for thyroid nodules primarily focus on malignancy screening but lack widespread clinical implementation.
There is a need to evaluate AI's efficacy in real-world clinical settings for managing thyroid nodules.

Purpose of the Study:

To assess the performance of an AI system in identifying potentially benign thyroid nodules that radiologists initially flagged as suspicious for malignancy.
The goal is to reduce unnecessary fine needle aspiration (FNA) procedures and optimize thyroid nodule management.

Main Methods:

A retrospective validation cohort of 4572 thyroid nodules with prior FNA was analyzed using a deep learning-based AI system.
The AI's diagnostic performance was evaluated for correctly identifying benign nodules and misclassifying malignant ones.
A comparison cohort was used to directly compare the AI's accuracy in identifying benign nodules against that of junior and senior radiologists.

Main Results:

The AI system correctly identified 86.8% of benign nodules and reduced unnecessary FNAs from 68.5% to 9.1%.
However, 8.6% of malignant nodules were misclassified as benign, primarily those with low or intermediate suspicion.
In a comparison cohort, the AI achieved an 81.4% accuracy for benign nodules, outperforming junior (40%) and senior (55%) radiologists, with a superior area under the curve (AUC) of 0.88.

Conclusions:

AI demonstrates potential as a "goalkeeper" tool, significantly decreasing unnecessary FNAs by accurately identifying benign thyroid nodules initially suspected of malignancy.
Despite AI's high performance, continued vigilance and active surveillance are essential due to the small possibility of misclassifying low-aggressiveness malignant nodules.