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Introduction and Hypothesis <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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Automated Evaluation of Female Pelvic Organ Descent on Transperineal Ultrasound: Model Development and Validation

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Shuangyu Wu,
  • Jiawei Wu,
  • Yuteng Xu,
  • Jiantao Tan,
  • Ruixuan Wang,
  • Xinling Zhang

摘要

Introduction and Hypothesis

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.

Methods

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.

Results

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.

Conclusions

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.