Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

Abstract

Background. Ultrasonography (US) is the most common method of identifying thyroid nodules, but US images require an experienced surgeon for identification. Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored. Objective. To clarify the accuracy of AI-based thyroid nodule US images for differentiating benign and malignant thyroid nodules. Methods. A search strategy of “subject terms + key words” was used to search PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine (CBM), and China National Knowledge Infrastructure (CNKI) for studies on AI-assisted diagnosis of thyroid nodules based on US images. The summarized receiver operating characteristic (SROC) curve and the pooled sensitivity and specificity were used to assess the performance of the diagnostic tests. The quality assessment of diagnostics accuracy studies-2 (QUADAS-2) tool was used to assess the methodological quality of the included studies. The Review Manager 5.3 and Stata 15 were used to process the data. Subgroup analysis was based on the integrity of data collection. Results. A total of 25 studies with 17,429 US images of thyroid nodules were included. AI-assisted diagnostic techniques had better diagnostic efficacy in the diagnosis of benign and malignant thyroid nodules: sensitivity 0.88 (95% CI: (0.85–0.90)), specificity 0.81 (95% CI: 0.74–0.86), diagnostic odds ratio (DOR) 30 (95% CI: 19–46). The SROC curve indicated that the area under the curve (AUC) was 0.92 (95% CI: 0.89–0.94). Threshold effect analysis showed a Spearman correlation coefficient: 0.17 < 0.5, suggesting no threshold effect for the included studies. After a meta-regression analysis of 4 different subgroups, the results showed a statistically significant effect of mean age ≥50 years on heterogeneity. Compared with studies with an average age of ≥50 years, AI-assisted diagnostic techniques had higher diagnostic performance in studies with an average age of <50 years (0.89 (95% CI: 0.87–0.92) vs. 0.80 (95% CI: 0.73–0.88)), (0.83 (95% CI: 0.77–0.88) vs. 0.73 (95% CI: 0.60–0.87)). Conclusions. AI-assisted diagnostic techniques had good diagnostic efficacy for thyroid nodules. For the diagnosis of <50 year olds, AI-assisted diagnostic technology was more effective in diagnosis.

Clinical Value of Artificial Intelligence‐Based Computer‐Aided Diagnosis System Versus Contrast‐Enhanced Ultrasound for Differentiation of Benign From Malignant Thyroid Nodules in Different Backgrounds

“…It was more objective on the segmentation results than manually initializing contours.AI can extract and quantify critical image information, transforming image diagnostics from subjective qualitative tasks into objective quantitative analysis. 26 Several recent studies show that it has diagnostic performance comparable to that of experienced radiologists. [27][28][29][30][31] Such a tool could offer great advantages.…”

“…Meanwhile, AI‐SONIC is based on automatic initial contour extraction and utilizes proper algorithms to obtain initial contours before applying active contour models to thyroid nodule segmentation. It was more objective on the segmentation results than manually initializing contours.AI can extract and quantify critical image information, transforming image diagnostics from subjective qualitative tasks into objective quantitative analysis 26 . Several recent studies show that it has diagnostic performance comparable to that of experienced radiologists 27–31 .…”

A systematic review on artificial intelligence techniques for detecting thyroid diseases

“…The paper also proposed future trends and challenges in the field and perspectives of computer-aided analysis to improve the efficiency of future methods for thyroid cancer diagnosis. In Ludwig et al (2023) and Xue et al (2022) the particular focus is on the usefulness of AI in ultrasonography for the diagnosis and characterization of thyroid cancer. Sorrenti et al (2022) also report an overview of the state of the art regarding AI implementation for thyroid nodule ultrasound characterization and cancer.…”

Artificial intelligence assisted diagnosis of early tc markers and its application

“…In addition, they proposed that the experienced radiologists may still have an advantage over AI algorithms in the real-time diagnostic process [ 68 ]. In the meta-analysis by Yu et al patients were grouped by age, and the analysis results showed that AI-assisted diagnostic techniques had higher diagnostic performance in subjects with an average age of < 50 years [ 69 ]. However, it is still important to note that benign nodules detected by ultrasound are likely to be histopathologically malignant, so the AI algorithms should not be expected to be foolproof.…”