Exploring the clinical diagnostic value of pelvic floor ultrasound images for pelvic organ prolapses through deep learning

Abstract

The 3D ultrasound features of patients with pelvic organ prolapses (POP) were analyzed using deep learning algorithms to explore the diagnostic efficacy of deep learning in identifying the types of POP and evaluate the feasibility of pelvic floor 3D ultrasound in diagnosing POP. Forty-four POP patients were included as the research objects, including 17 cases of anterior cavity prolapse, 23 cases of middle cavity prolapse, and four cases of posterior cavity prolapse. Twenty healthy women without POP were the control group. The 3D perineum ultrasound images of the patients were collected. A feature classification model for pelvic floor ultrasound images was built using deep learning algorithms. The proposed model was then compared with three convolutional neural network (CNN) models: support vector machine (SVM), radial basis function (RBF), and K-nearest neighbor (KNN), to evaluate its diagnostic efficiency in identifying the types of POP. Furthermore, the POP-Q score was performed based on ultrasound features. The results showed that the area under the curve (AUC) of the deep learning model was 0.79, and the 95% confidence interval was 0.76–0.80, higher than those of the CNN models. The accuracy (0.86), sensitivity (0.89), and specificity (0.84) of the deep learning model were higher than those of the three CNN models. The pelvic mouths of the study group and the control group were significantly more extensive than that of the nonproductive group (p < 0.05). The levator ani muscle of the POP group was significantly more extensive than that of the control group, and the difference was statistically significant (p < 0.05). The model based on deep learning has high efficiency in identifying the types of POP. The 3D ultrasound examination has important clinical significance for POP diagnosis.