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Updated: Aug 9, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Clinical Value of Artificial Intelligence-Based Computer-Aided Diagnosis System Versus Contrast-Enhanced Ultrasound
Ting Liu1, Chuang Wu2, Guojuan Wang1
1Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China.
Objectives:
The aim of this study was to compare the value of the AI-SONIC ultrasound-assisted diagnosis system versus contrast-enhanced ultrasound (CEUS) for differential diagnosis of thyroid nodules in diffuse and non-diffuse backgrounds.
Methods:
A total of 555 thyroid nodules with pathologically confirmed diagnosis were included in this retrospective study. The diagnostic efficacies of AI-SONIC and CEUS for differentiating benign from malignant nodules in diffuse and non-diffuse backgrounds were evaluated, with pathological diagnosis as the gold standard.
Results:
The agreement between AI-SONIC diagnosis and pathological diagnosis was moderate in diffuse backgrounds (κ = 0.417) and almost perfect in non-diffuse backgrounds (κ = 0.81). The agreement between CEUS diagnosis and pathological diagnosis was substantial in diffuse backgrounds (κ = 0.684) and moderate in non-diffuse backgrounds (κ = 0.407). In diffuse backgrounds, AI-SONIC had slightly higher sensitivity (95.7 vs 89.4%, P = .375), but CEUS had significantly higher specificity (80.0 vs 40.0%, P = .008). In non-diffuse background, AI-SONIC had significantly higher sensitivity (96.2 vs 73.4%, P < .001), specificity (82.9 vs 71.2%, P = .007), and negative predictive value (90.3 vs 53.3%, P < .001).
Conclusion:
In non-diffuse backgrounds, AI-SONIC is superior to CEUS for differentiating malignant from benign thyroid nodules. In diffuse backgrounds, AI-SONIC could be useful for screening of cases to detect suspicious nodules requiring further examination by CEUS.
