The aim of this study was to evaluate the accuracy of deep learning using the convolutional neural network VGGNet model in distinguishing benign and malignant thyroid nodules based on ultrasound images.

Relevant studies were selected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases, which used the deep learning-related convolutional neural network VGGNet model to classify benign and malignant thyroid nodules based on ultrasound images. Cytology and pathology were used as gold standards. Furthermore, reported eligibility and risk bias were assessed using the QUADAS-2 tool, and the diagnostic accuracy of deep learning VGGNet was analyzed with pooled sensitivity, pooled specificity, diagnostic odds ratio, and the area under the curve.

A total of 11 studies were included in this meta-analysis. The overall estimates of sensitivity and specificity were 0.87 [95% CI (0.83, 0.91)] and 0.85 [95% CI (0.79, 0.90)], respectively. The diagnostic odds ratio was 38.79 [95% CI (22.49, 66.91)]. The area under the curve was 0.93 [95% CI (0.90, 0.95)]. No obvious publication bias was found.

Deep learning using the convolutional neural network VGGNet model based on ultrasound images performed good diagnostic efficacy in distinguishing benign and malignant thyroid nodules.

https://www.crd.york.ac.nk/prospero, identifier CRD42022336701.

本研究的目的是评估使用卷积神经网络 VGGNet 模型的深度学习在基于超声图像区分良性和恶性甲状腺结节中的准确性。

相关研究选自PubMed、Embase、Cochrane Library、CNKI、万方数据库，采用深度学习相关的卷积神经网络VGGNet模型，基于超声图像对甲状腺结节进行良恶性分类。细胞学和病理学被用作金标准。此外，使用 QUADAS-2 工具评估报告的资格和风险偏倚，并使用汇总灵敏度、汇总特异性、诊断优势比和曲线下面积分析深度学习 VGGNet 的诊断准确性。

本荟萃分析共纳入 11 项研究。敏感性和特异性的总体估计值分别为 0.87 [95% CI (0.83, 0.91)] 和 0.85 [95% CI (0.79, 0.90)]。诊断优势比为 38.79 [95% CI (22.49, 66.91)]。曲线下面积为 0.93 [95% CI (0.90, 0.95)]。未发现明显的发表偏倚。

使用基于超声图像的卷积神经网络 VGGNet 模型的深度学习在区分良性和恶性甲状腺结节方面表现出良好的诊断效果。

https://www.crd.york.ac.nk/prospero, 标识符 CRD42022336701。