Combining pelvic floor ultrasonography with deep learning to diagnose anterior compartment organ prolapse.

Background: Anterior compartment prolapse is a common pelvic organ prolapse (POP), which occurs frequently among middle-aged and elderly women and can cause urinary incontinence, perineal pain and swelling, and seriously affect their physical and mental health. At present, pelvic floor ultrasound is the primary examination method, but it is not carried out by many primary medical institutions due to the significant shortcomings of training in the early stage and the variable image quality. There has been great progress in the application of deep learning (DL) in image-based diagnosis in various clinical contexts. The main purpose of this study was to improve the speed and reliability of pelvic floor ultrasound diagnosis of POP by training neural networks to interpret ultrasound images, thereby facilitating the diagnosis and treatment of POP in primary care. Methods: This retrospective study analyzed medical records of women with anterior compartment organ prolapse (n=1,605, mean age 45.1±12.2 years) or without (n=200, mean age 38.1±13.4 years), who were examined at West China Second University Hospital between March 2019 and September 2021. Static ultrasound images of the anterior chamber of the pelvic floor (5,281 abnormal, 535 normal) were captured at rest and at maximal Valsalva motion, and four convolutional neural network (CNN) models, AlexNet, VGG-16, ResNet-18, and ResNet-50, were trained on 80% of the images, then internally validated on the other 20%. Each model was trained in two ways: through a random initialization parameter training method and through a transfer learning method based on ImageNet pre-training. The diagnostic performance of each network was evaluated according to accuracy, precision, recall and F1-score, and the receiver operating characteristic (ROC) curve of each network in the training set and validation set was drawn and the area under the curve (AUC) was obtained. Results: All four models, regardless of training method, achieved recognition accuracy of >91%, whereas transfer learning led to more stable and effective feature extraction. Specifically, ResNet-18 and ResNet-50 performed better than AlexNet and VGG-16. However, the four networks learned by transfer all showed fairly high AUCs, with the ResNet-18 network performing the best: it read images in 13.4 msec and provided recognition an accuracy of 93.53% along with an AUC of 0.852. Conclusions: Combining DL with pelvic floor ultrasonography can substantially accelerate diagnosis of anterior compartment organ prolapse in women while improving accuracy.