Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study
Huiling Xiang, Xi Wang, Min Xu, Yuhua Zhang, Shu‐E Zeng, Chunyan Li, Lixian Liu, Tingting Deng, Guoxue Tang, Cuiju Yan, Jinjing Ou, Qingguang Lin, Jiehua He, Peng Sun, Anhua Li, Hao Chen, Pheng‐Ann Heng, Xi Lin
Abstract
Purpose: To evaluate the diagnostic performance of a deep learning (DL) model for breast US across four hospitals and assess its value to readers with different levels of experience. Materials and Methods: = 45 909, March 2011-August 2018), acquired with 42 types of US machines, of 9895 pathologic analysis-confirmed breast lesions in 8797 patients (27 men and 8770 women; mean age, 47 years ± 12 [SD]). With and without assistance from the DL model, three novice readers with less than 5 years of US experience and two experienced readers with 8 and 18 years of US experience, respectively, interpreted 1024 randomly selected lesions. Differences in the areas under the receiver operating characteristic curves (AUCs) were tested using the DeLong test. Results: = .08). Conclusion: © RSNA, 2023.