To evaluate the auxiliary diagnostic value of an artificial intelligence (AI)-based automatic recognition model for cystocele in pelvic floor ultrasound examination.

A total of 200 female outpatients who underwent pelvic floor ultrasound examination at Shengjing Hospital of China Medical University between July and December 2024 were included. Among them, 103 cases were diagnosed with cystocele and 97 had normal anterior pelvic compartment structures. The subjects were randomly divided into a training set (n=140) and a testing set (n=60) in a 7∶3 ratio. In the training set, 73 had cystocele and 67 were normal; in the testing set, both groups included 30 cases. Dynamic pelvic floor ultrasound images at rest and during maximal Valsalva maneuver were collected. Urethral contours were manually delineated, and 13 urethral mobility parameters were extracted. Based on these features, support vector machine (SVM) and random forest (RF) models were trained using five-fold cross-validation, and the resulting five sets of weights were used to evaluate diagnostic performance in the testing set. The study was approved by the Ethics Committee of Shengjing Hospital, China Medical University (Approval No. 2024PS1218K), and all participants provided written informed consent.

①There were no statistically significant differences in age, body mass index (BMI), height, weight, gravidity, parity, ethnicity, or occupational composition between the training and testing sets (P>0.05). ②In the testing set, the SVM model demonstrated an accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC) of 0.783, 0.718, 0.933, 0.811, and 0.893, respectively, all superior to those of the RF model (0.733, 0.659, 0.966, 0.783, and 0.876). Among the five SVM model, the first fold performed best with a ROC-AUC of 0.904. ③Confusion matrix analysis showed that the correct identification rates of cystocele by the SVM and RF models were 93.3% (28/30) and 96.7% (29/30), respectively; for normal anterior compartment structures, the rates were 63.3% (19/30) and 50.0% (15/30), respectively.

The AI-based automatic recognition model using full-length urethral dynamic parameters demonstrates favorable diagnostic performance in identifying cystocele and may serve as an objective auxiliary tool to conventional pelvic floor ultrasound. It holds promise for early screening, subtype classification, and postoperative follow-up of cystocele. However, further studies with larger sample sizes and multi-center validation are required to optimize the algorithm and facilitate clinical translation.