Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign

Abstract

Objectives: Pouch of Douglas (POD) obliteration is a severe consequence of inflammation in the pelvis, often seen in patients with endometriosis. The sliding sign is a dynamic transvaginal ultrasound (TVS) test that can diagnose POD obliteration. We aimed to develop a deep learning (DL) model to automatically classify the state of the POD using recorded videos depicting the sliding sign test. Methods: Two expert sonologists performed, interpreted, and recorded videos of consecutive patients from September 2018 to April 2020. The sliding sign was classified as positive (i.e. normal) or negative (i.e. abnormal; POD obliteration). A DL model based on a temporal residual network was prospectively trained with a dataset of TVS videos. The model was tested on an independent test set and its diagnostic accuracy including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value (PPV/NPV) was compared to the reference standard sonologist classification (positive or negative sliding sign). Results: In a dataset consisting of 749 videos, a positive sliding sign was depicted in 646 (86.2%) videos, whereas 103 (13.8%) videos depicted a negative sliding sign. The dataset was split into training (414 videos), validation (139), and testing (196) maintaining similar positive/negative proportions. When applied to the test dataset using a threshold of 0.9, the model achieved: AUC 96.5% (95% CI: 90.8–100.0%), an accuracy of 88.8% (95% CI: 83.5–92.8%), sensitivity of 88.6% (95% CI: 83.0–92.9%), specificity of 90.0% (95% CI: 68.3–98.8%), a PPV of 98.7% (95% CI: 95.4–99.7%), and an NPV of 47.7% (95% CI: 36.8–58.2%). Conclusions: We have developed an accurate DL model for the prediction of the TVS-based sliding sign classification.

Keywords

• sliding sign
• pouch of douglas obliteration
• pelvic adhesions
• endometriosis
• ultrasonography
• machine learning
• deep learning
• artificial intelligence
• computer-aided diagnosis