AUTHOR=Wang Xi , Qi Yiting , Zhang Xin , Liu Fang , Li Jia 

TITLE=Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1570811

DOI=10.3389/fendo.2025.1570811

ISSN=1664-2392

ABSTRACT=ObjectiveThis meta-analysis aims to evaluate the diagnostic performance of ultrasound (US)-based artificial intelligence (AI) in assessing cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).MethodsA comprehensive literature search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify relevant studies published up to November 19, 2024. Studies focused on the diagnostic performance of AI in the detection of CLNM of PTC were included. A bivariate random-effects model was used to calculate the pooled sensitivity and specificity, both with 95% confidence intervals (CI). The I2 statistic was used to assess heterogeneity among studies.ResultsAmong the 593 studies identified, 27 studies were included (involving over 23,170 patients or images). For the internal validation set, the pooled sensitivity, specificity, and AUC for detecting CLNM of PTC were 0.80 (95% CI: 0.75–0.84), 0.83 (95% CI: 0.80–0.87), and 0.89 (95% CI: 0.86–0.91), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.77 (95% CI: 0.49–0.92), 0.82 (95% CI: 0.75–0.88), and 0.86 (95% CI: 0.83–0.89), respectively. For US physicians, the overall sensitivity, specificity, and AUC for detecting CLNM were 0.51 (95% CI: 0.38–0.64), 0.84 (95% CI: 0.76–0.89), and 0.77 (95% CI: 0.73–0.81), respectively.ConclusionUS-based AI demonstrates higher diagnostic performance than US physicians. However, the high heterogeneity among studies and the limited number of externally validated studies constrain the generalizability of these findings, and further research on external validation datasets is needed to confirm the results and assess their practical clinical value.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024625725, identifier CRD42024625725.