AI System Brings Standardized Cystocele Diagnosis to Dynamic Pelvic Ultrasound

Artificial intelligence has taken another step into the exam room, and this time the target is one of the most quietly common conditions in women’s health: cystocele, the herniation of the bladder into the front wall of the vagina that affects a large share of women who have given birth. In a study published in the Journal of Medical Systems, a team of engineers and clinicians from Northeastern University and Shengjing Hospital of China Medical University in Shenyang, China, describes an AI-assisted clinical decision support system that can automatically classify cystocele severity on dynamic transperineal ultrasound, a real-time imaging technique that captures the pelvic floor in motion. The system, called Green-AttGRU, was tested on nearly 900 patients and, in a controlled reader study, measurably improved the accuracy, consistency, and speed of human radiologists interpreting these challenging scans.

The clinical problem the researchers set out to solve is deceptively simple to state but difficult to solve in practice. Dynamic transperineal ultrasound, or TPUS, is a noninvasive imaging method in which a probe is placed on the perineum, the area between the vagina and the anus, to visualize the bladder, urethra, and surrounding pelvic structures. During the examination, the patient is asked to perform the Valsalva maneuver, forcefully exhaling against a closed airway as if straining, which increases abdominal pressure and causes pelvic organs to descend. The sonographer must then identify the single most informative frame from the resulting video, trace the position of the urethrovesical junction, the point where the urethra meets the bladder, and grade the degree of descent according to the Green classification system, a scheme first proposed in 1975 that divides cystocele severity into types I, II, and III based on the angle and orientation of the urethra relative to the bladder base.

Every one of those steps depends on human judgment. Selecting the peak Valsalva frame requires recognizing the moment of maximal descent amid a noisy, rapidly changing image sequence. Placing landmarks on the urethrovesical junction demands anatomical expertise, and small errors in landmark placement can shift a patient from one Green type to another. Studies of pelvic floor ultrasound have long documented substantial variability between observers, and the classification is further complicated by confounders such as levator co-activation, in which involuntary contraction of the pelvic floor muscles during straining masks the true extent of organ descent. The result is that Green classification, despite its clinical value in guiding surgical planning for anterior vaginal wall prolapse, remains operator-dependent in a way that many other ultrasound measurements do not.

The Green-AttGRU system was designed to compress that entire manual workflow into an automated pipeline. The architecture combines a deep convolutional neural network for visual feature extraction with a gated recurrent unit, a type of recurrent neural network well suited to sequential data, augmented with an attention mechanism that allows the model to focus on the most diagnostically relevant frames in the ultrasound video. In practical terms, the network watches the whole Valsalva sequence the way a sonographer would, learns which frames capture the moment of maximal bladder neck descent, localizes the urethrovesical junction, and outputs a Green type classification without any manual frame selection or landmark tracing. The name reflects this design: the attention-enhanced gated recurrent unit sits at the heart of the classification engine.

To train and validate the system, the team assembled a dataset of 881 patients examined at Shengjing Hospital, a tertiary referral center. Of these, 688 patients formed a retrospective development cohort used to train the model, while 193 patients were enrolled prospectively and formed an independent test cohort that the model had never seen during training. This separation matters enormously in machine learning for medicine, because models that are evaluated only on the data they were trained on routinely overstate their performance. The prospective design, in which patients were enrolled and scanned after the model architecture was fixed, provides a more honest estimate of how the system would behave in clinical use. The study was approved by the hospital’s ethics committee and conducted in accordance with the Declaration of Helsinki, with written informed consent obtained from prospective participants.

The standalone performance of Green-AttGRU on the complete prospective test cohort was strong. The model achieved a macro-averaged area under the receiver operating characteristic curve, a measure of diagnostic discrimination across all severity classes, of 0.939, with a 95 percent confidence interval of 0.897 to 0.971. Its overall accuracy was 0.902, meaning it agreed with the reference standard in roughly nine out of ten cases. For a three-class classification task performed on dynamic ultrasound video, where the boundaries between Green types are defined by continuous anatomical angles that patients can sit near, those numbers place the automated system in the range of what experienced human readers can achieve, but with the crucial advantage of perfect repeatability: the same input always produces the same output.

The more clinically revealing experiment was the reader study, which asked whether the AI could make human radiologists better rather than simply replace them. Four radiologists, two junior and two intermediate in experience, independently interpreted 67 prospective patient examinations under two conditions: unaided, and with the AI system’s output available as decision support. Without AI assistance, the four readers achieved overall accuracies ranging from 0.761 to 0.821, with macro-averaged F1 scores, which balance precision and recall across classes, between 0.660 and 0.777. With the AI’s classification available, accuracy rose to a range of 0.851 to 0.881, and macro-F1 climbed to 0.820 to 0.860. The improvement was consistent across readers, suggesting that the benefit was not confined to the least experienced members of the panel.

Perhaps the most striking findings concerned agreement and speed. Before AI assistance, the four radiologists agreed with one another only moderately, with a Fleiss’ kappa, a statistic that measures inter-rater agreement beyond chance, of 0.453. After consulting the AI, that figure jumped to 0.786, indicating substantial agreement. In other words, the system did not just make the readers more accurate; it made them more consistent with one another, converging on a shared interpretation of ambiguous scans. At the same time, the pooled median interpretation time per case fell from 26.7 seconds to 9.9 seconds, a reduction of more than 60 percent. For a busy pelvic floor imaging service, that difference compounds quickly, and it points to a workflow benefit that goes beyond diagnostic quality alone.

The study’s authors are careful to frame these results as evidence of preliminary feasibility rather than proof of readiness for unsupervised clinical deployment. The data come from a single tertiary referral hospital, and the reader study involved eight radiologists in total across two experience levels, a sample that cannot capture the full spectrum of expertise and scanning conditions found in the wider clinical world. The system was also designed and evaluated for one specific task, Green classification of the anterior compartment, whereas a complete pelvic floor ultrasound assessment involves additional measurements, including hiatal dimensions and organ descent at rest and on straining, that the current pipeline does not address. External validation at multiple centers, with different scanner hardware and patient populations, remains the necessary next step before any regulatory or guideline body would consider routine use.

Even with those caveats, the work adds to a rapidly growing body of evidence that deep learning can standardize the interpretation of pelvic floor ultrasound, a field that has historically lagged behind obstetric imaging in automation. Recent studies have demonstrated deep learning models for identifying pelvic floor organs in the midsagittal plane, for automating the evaluation of female pelvic organ descent, and for enhancing three-dimensional transperineal ultrasound biometry in prolapse assessment. What distinguishes the present study is its end-to-end scope, spanning frame selection, landmark localization, and classification within a single workflow-oriented system, and its prospective reader study design, which follows the kind of evaluation framework that clinical prediction model reporting guidelines now recommend. If subsequent multicenter studies replicate these results, AI-assisted Green classification could become a practical tool for reducing variability in pelvic floor imaging, shortening examination times, and ultimately helping clinicians choose the right surgical approach for the millions of women whose bladder support fails them.

Subject of Research: Development and prospective validation of an AI-assisted clinical decision support system for automated Green classification of cystocele on dynamic transperineal ultrasound.

Article Title: An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound

Article References: Zhu, H., Geng, X., Zhou, H., Guo, W., Dai, Y., Zhang, H., Dong, M., Li, H., & Wang, X. (2026). An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound. Journal of Medical Systems, 50(1), Article 127. https://doi.org/10.1007/s10916-026-02453-7

Image Credits: AI Generated

DOI: 10.1007/s10916-026-02453-7

Keywords: cystocele, transperineal ultrasound, Green classification, artificial intelligence, clinical decision support, pelvic organ prolapse, deep learning, reader study, urethrovesical junction, Valsalva maneuver, radiology, pelvic floor