Wu Shuangyu, Wu Jiawei, Xu Yuteng, Tan Jiantao, Wang Ruixuan, Zhang Xinling
Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Int Urogynecol J. 2025 Jun 28. doi: 10.1007/s00192-025-06211-0.
Transperineal ultrasound (TPUS) is a widely used tool for evaluating female pelvic organ prolapse (POP), but its accurate interpretation relies on experience, causing diagnostic variability. This study aims to develop and validate a multi-task deep learning model to automate POP assessment using TPUS images.
TPUS images from 1340 female patients (January-June 2023) were evaluated by two experienced physicians. The presence and severity of cystocele, uterine prolapse, rectocele, and excessive mobility of perineal body (EMoPB) were documented. After preprocessing, 1072 images were used for training and 268 for validation. The model used ResNet34 as the feature extractor and four parallel fully connected layers to predict the conditions. Model performance was assessed using confusion matrix and area under the curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) visualized the model's focus areas.
The model demonstrated strong diagnostic performance, with accuracies and AUC values as follows: cystocele, 0.869 (95% CI, 0.824-0.905) and 0.947 (95% CI, 0.930-0.962); uterine prolapse, 0.799 (95% CI, 0.746-0.842) and 0.931 (95% CI, 0.911-0.948); rectocele, 0.978 (95% CI, 0.952-0.990) and 0.892 (95% CI, 0.849-0.927); and EMoPB, 0.869 (95% CI, 0.824-0.905) and 0.942 (95% CI, 0.907-0.967). Grad-CAM heatmaps revealed that the model's focus areas were consistent with those observed by human experts.
This study presents a multi-task deep learning model for automated POP assessment using TPUS images, showing promising efficacy and potential to benefit a broader population of women.
经会阴超声（TPUS）是评估女性盆腔器官脱垂（POP）的一种广泛使用的工具，但其准确解读依赖于经验，会导致诊断的变异性。本研究旨在开发并验证一种多任务深度学习模型，以利用TPUS图像自动评估POP。
由两名经验丰富的医生对1340名女性患者（2023年1月至6月）的TPUS图像进行评估。记录膀胱膨出、子宫脱垂、直肠膨出和会阴体过度活动（EMoPB）的存在情况和严重程度。经过预处理后，1072幅图像用于训练，268幅用于验证。该模型使用ResNet34作为特征提取器和四个并行的全连接层来预测病情。使用混淆矩阵和曲线下面积（AUC）评估模型性能。梯度加权类激活映射（Grad-CAM）可视化模型的关注区域。
该模型表现出强大的诊断性能，准确率和AUC值如下：膀胱膨出，0.869（95%CI，0.824-0.905）和0.947（95%CI，0.930-0.962）；子宫脱垂，0.799（95%CI，0.746-0.842）和0.931（95%CI，0.911-0.948）；直肠膨出，0.978（95%CI，0.952-0.990）和0.892（95%CI，0.849-0.927）；以及EMoPB，0.869（95%CI，0.824-0.905）和0.942（95%CI，0.907-0.967）。Grad-CAM热图显示，模型的关注区域与人类专家观察到的区域一致。
本研究提出了一种利用TPUS图像自动评估POP的多任务深度学习模型，显示出有前景的疗效和使更广泛女性群体受益的潜力。
