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Updated: Sep 19, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Diagnostic performance of the ultrasound -based artificial intelligence diagnostic system in predicting cervical
Xueyao Tang1, Hong Zhou1, Ying Liu1
1Department of Ultrasound, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
Abstract:
BackgroundThe incidence of cervical lymph node metastasis (CLNM) in thyroid cancer (TC) is high. Accurate preoperative diagnosis of CLNM is critical to reduce unnecessary lymph node dissection and complications for TC patients. Ultrasound (US)-based artificial intelligence (AI) systems show promise for CLNM prediction, but their diagnostic performance requires systematic evaluation.MethodsA comprehensive search of four electronic databases (Web of Science, Embase, PubMed, and Cochrane Library) was conducted from inception to 30 December 2023. The random-effects model was chosen to calculate the pooled diagnostic indicators. Sensitivity analysis and heterogeneity test were conducted.ResultsAmong 19 included studies, the AI system demonstrated pooled sensitivity, specificity, area under the curve (AUC) were 0.76 (95% condidence interval (CI): 0.71-0.80), 0.78 (95% CI: 0.74-0.82), and 0.84 (95% CI: 0.15-0.99), respectively. The sensitivity, specificity and AUC in clinically node-negative (cN0) patients were 0.73 (95% CI: 0.68-0.77), 0.81 (95% CI: 0.76-0.85) and 0.83 (95% CI: 0.14-0.99). The sensitivity, specificity and AUC for the central CLNM were 0.73 (95% CI: 0.69-0.77), 0.77 (95% CI: 0.72-0.81) and 0.81 (95% CI: 0.14-0.99). Multi-center designed studies yielded higher sensitivity (0.79 vs. 0.75, p < 0.01) and specificity (0.79 vs. 0.78, p < 0.01) than single-center designs. Deep learning (DL) yielded higher sensitivity (0.79 vs. 0.74, p < 0.01) and specificity (0.83 vs. 0.75, p < 0.01) than classic machine learning. Studies published after 2022 yielded higher sensitivity (0.77 vs. 0.74, p < 0.01) than before 2022. Studies from China had lower specificity than studies from other countries (0.78 vs. 0.80, p = 0.01). Models incorporating multimodal features outperformed unimodal US (specificity: 0.79 vs. 0.75, p < 0.01).ConclusionUS-based AI systems exhibit favorable predictive value for CLNM in TC, particularly with DL and multimodal designs, potentially reducing overtreatment. Prospective validation is needed prior to clinical adoption.
