Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning.

Abstract

We aimed to develop a deep learning-based multi-label classification model to simultaneously diagnose three types of pelvic organ prolapse using stress magnetic resonance imaging (MRI). Our dataset consisted of 213 midsagittal labeled MR images at maximum Valsalva. For each MR image, the two endpoints of the sacrococcygeal inferior-pubic point line were auto-localized. Based on this line, a region of interest was automatically selected as input to a modified deep learning model, ResNet-50, fordiagnosis. An unlabeled MRI dataset, a public dataset, and a synthetic dataset were used along with the labeled image dataset to train the model through a novel training strategy. We conducted a fivefold cross-validation and evaluated the classification results using precision, recall, F1 score, and area under the curve (AUC). The average precision, recall, F1 score, and AUC of our proposed multi-label classification model for the three types of prolapse were 0.84, 0.72, 0.77, and 0.91 respectively, which were improved from 0.64, 0.53, 0.57, and 0.83 from the original ResNet-50. Classification took 0.18s to diagnose one patient. The proposed deep learning-based model were demonstrated feasible and fast in simultaneously diagnosing three types of prolapse based on pelvic floor stress MRI, which could facilitate computer-aided prolapse diagnosis and treatment planning.