Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis - Zhan, Jianfeng; Zhang, Jian; Zhu, Shaoqi; Ni, Lin; Zhang, Chen; Hu, Jia (Frontiers in Oncology, 2025)
BackgroundNowadays, artificial intelligence (AI) diagnostic models based on ultrasound features have been gradually integrated into the evaluation of thyroid nodules. However, the diagnostic effects of different AI-assisted diagnosis methods vary greatly.ObjectiveThis study aims to systematically evaluate the performance of the ultrasound-based artificial intelligence diagnostic models in differentiating benign and malignant thyroid nodules and to determine the most effective diagnostic model.MethodsWe conducted a comprehensive literature search in PubMed, Web of Science, and the Cochrane Library using subject-specific keywords to identify studies on AI-assisted thyroid nodule diagnosis. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Meta-analysis was performed using Meta-Disc 1.4, Review Manager 5.4, R 4.4.2, and Stata 17.0. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (SROC-AUC) with 95% confidence intervals (CI) were calculated. Subgroup analyses and clinical applicability assessments were conducted.ResultsTwenty-eight studies involving 134,028 patients, 158,161 thyroid nodules, and 529,479 ultrasound images were included. The AI-assisted diagnostic system demonstrated high diagnostic performance: pooled sensitivity = 0.89 (95% CI: 0.87–0.91), specificity = 0.84 (0.80–0.88), positive likelihood ratio (PLR) = 5.60 (4.40–7.20), negative likelihood ratio (NLR) = 0.13 (0.10–0.16), DOR = 43.94 (30.11–64.14), and SROC-AUC = 0.93 (0.91–0.95). The threshold effect analysis (Spearman correlation = -0.18, P > 0.05) indicated no significant heterogeneity. The diagnostic accuracy is higher in Asian countries, in prospective and multicenter designs, with external validation sets, without cross-validation, with deep learning, and in postoperative patient subgroups. Additionally, improved performance was observed in cohorts with smaller nodule diameters (<20 mm), higher malignancy rates, older patient age (≥50 years), and higher female proportions, though heterogeneity remained significant. Univariate and multivariate meta-regression analyses identified AI type, malignancy rate of nodules as significant sources of heterogeneity. Notably, the EDLC-TN model showed the highest diagnostic accuracy.ConclusionAI-assisted diagnostic techniques demonstrate significant potentialin thyroid nodule evaluation, with the EDLC-TN model showing particularly high clinical utility. Optimal diagnostic performance was observed for nodules <20 mm in diameter and in patients aged ≥50 years.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024581421, identifier CRD42024581421.