Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging - Xiangyu Wang; Liang Wang; Xin Hou; Jingfang Li; Jin Li; Xiangyi Ma (BMC Medical Imaging)
Abstract Background The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it’s difficult to make an accurate differential diagnosis. This study is intended to assess the performance of multiple deep learning models based on ultrasonographic images for distinguishing longitudinal vaginal septum and oblique vaginal septum. Methods The cases and ultrasound images of longitudinal vaginal septum and oblique vaginal septum were collected. Two convolutional neural network (CNN)-based models (ResNet50 and ConvNeXt-B) and one base resolution variant of vision transformer (ViT)-based neural network (ViT-B/16) were selected to construct ultrasonographic classification models. The receiver operating curve analysis and four indicators including accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the diagnostic performance of deep learning models. Results A total of 70 cases with 426 ultrasound images were included for deep learning models construction using 5-fold cross-validation. Convolutional neural network-based models (ResNet50 and ConvNeXt-B) presented significantly better case-level discriminative efficacy with accuracy of 0.842 (variance, 0.004, 95%CI, [0.639–0.997]) and 0.897 (variance, 0.004, [95%CI, 0.734-1.000]), specificity of 0.709 (variance, 0.041, [95%CI, 0.505–0.905]) and 0.811 (variance, 0.017, [95%CI, 0.622–0.979]), and AUC of 0.842 (variance, 0.004, [95%CI, 0.639–0.997]) and 0.897 (variance, 0.004, [95%CI, 0.734-1.000]) than transformer-based model (ViT-B/16) with its accuracy of 0.668 (variance, 0.014, [95%CI, 0.407–0.920]), specificity of 0.572 (variance, 0.024, [95%CI, 0.304–0.831]) and AUC of 0.681 (variance, 0.030, [95%CI, 0.434–0.908]). There was no significance of AUC between ConvNeXt-B and ResNet50 (P = 0.841). Conclusions Convolutional neural network-based model (ConvNeXt-B) shows promising capability of discriminating longitudinal and oblique vaginal septa ultrasound images and is expected to be introduced to clinical ultrasonographic diagnostic system.