AUTHOR=Hu Ping , Gao Yanjuan , Zhang Yiqian , Sun Kui 

TITLE=Ultrasound image-based deep learning to differentiate tubal-ovarian abscess from ovarian endometriosis cyst

JOURNAL=Frontiers in Physiology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1101810

DOI=10.3389/fphys.2023.1101810

ISSN=1664-042X

ABSTRACT=<p><bold>Objectives:</bold> We developed ultrasound (US) image-based convolutional neural networks (CNNs) to distinguish between tubal-ovarian abscess (TOA) and ovarian endometriosis cyst (OEC).</p><p><bold>Methods:</bold> A total of 202 patients who underwent US scanning and confirmed tubal-ovarian abscess or ovarian endometriosis cyst by pathology were enrolled in retrospective research, in which 171 patients (from January 2014 to September 2021) were considered the primary cohort (training, validation, and internal test sets) and 31 patients (from September 2021 to December 2021) were considered the independent test cohort. There were 68 tubal-ovarian abscesses and 89 OEC, 4 TOA and 10 OEC, and 10 TOA and 21 OEC patients belonging to training and validation sets, internal sets, and independent test sets, respectively. For the model to gain better generalization, we applied the geometric image and color transformations to augment the dataset, including center crop, random rotation, and random horizontal flip<italic>.</italic> Three convolutional neural networks, namely, ResNet-152, DenseNet-161, and EfficientNet-B7 were applied to differentiate tubal-ovarian abscess from ovarian endometriosis cyst, and their performance was compared with three US physicians and a clinical indicator of carbohydrate antigen 125 (CA125) on the independent test set. The area under the receiver operating characteristic curves (AUROCs) of accuracy, sensitivity, and specificity were used to evaluate the performance.</p><p><bold>Results:</bold> Among the three convolutional neural networks, the performance of ResNet-152 was the highest, with AUROCs of 0.986 (0.954–1). The AUROCs of the three physicians were 0.781 (0.620–0.942), 0.738 (0.629–848), and 0.683 (0.501–0.865), respectively. The clinical indicator CA125 achieved only 0.564 (0.315–0.813).</p><p><bold>Conclusion:</bold> We demonstrated that the CNN model based on the US image could discriminate tubal-ovarian abscess and ovarian endometriosis cyst better than US physicians and CA125. This method can provide a valuable predictive reference for physicians to screen tubal-ovarian abscesses and ovarian endometriosis cysts in time.</p>