Diagnostic efficacy of ultrasound combined with magnetic resonance imaging in diagnosis of deep pelvic endometriosis under deep learning

This article was to explore the application of deep learning algorithms in the classification and diagnosis of deep pelvic endometriosis (DPE) by ultrasound and magnetic resonance imaging (MRI). Vaginal ultrasound (VU) and MRI images of 118 patients with DPE and 206 patients with other gynaecological diseases who were treated in our hospital from January 2015 to January 2018 were analysed. Firstly, the global average pooling (GAP) was introduced based on the visual geometry group (VGG) network to design the VGG-GAP model for VU image recognition. Next, the C3D model was improved as the IC3D model for MRI image recognition. The diagnostic values of patients with DPE based on classified VU and MRI images were compared and analysed. The results revealed that the classification accuracy of the VGG-GAP model in the VU image was 96.5%, and the classification accuracy of the IC3D model in the MRI image was 99.2%. The diagnosis accuracy using VU images and MRI images was 90.68% and 92.37%, respectively. Based on this, the classification of VU or MRI images based on deep learning algorithms could provide a basis for improving the diagnosis efficiency of DPE. The value of MRI in the diagnosis of DPE was significantly higher than that of VU. Similar content being viewed by others

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Author Min Liu declares that he has no conflict of interest. Author Yan Chen declares that he has no conflict of interest. Author Suhui He declares that he has no conflict of interest. Author Yan Lin declares that he has no conflict of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions About this article Cite this article Yang, M., Liu, M., Chen, Y. et al. Diagnostic efficacy of ultrasound combined with magnetic resonance imaging in diagnosis of deep pelvic endometriosis under deep learning. J Supercomput 77, 7598–7619 (2021). https://doi.org/10.1007/s11227-020-03535-0 Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s11227-020-03535-0