Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis - Zhu, Pei-Shan; Zhang, Yu-Rui; Ren, Jia-Yu; Li, Qiao-Li; Chen, Ming; Sang, Tian; Li, Wen-Xiao; Li, Jun; Cui, Xin-Wu (Frontiers in Oncology, 2022)
Objective: To evaluate the accuracy of the deep learning using convolutional neural network VGGNet model in distinguishing benign and malignant thyroid nodules based on ultrasound images. Methods: Relevant studies were selected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wan Fang database, which used deep learning-related convolutional neural network VGGNet model to classify benign and malignant of 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 the pooled sensitivity, pooled specificity, diagnostic odds ratio, and the area under the curve. Results: 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. Conclusion: Deep learning using convolutional neural network VGGNet model based on ultrasound images performed good diagnostic efficacy in distinguishing benign and malignant thyroid nodules.