Scholarlink

Introduction and Hypothesis <p>We developed a dual-task deep-learning model, termed FD-Net, which utilizes fused two-dimensional (2D) and three-dimensional (3D) ultrasound images to simultaneously automate cystocele typing and grading, and evaluated its diagnostic performance.</p> Methods <p>We retrospectively included 625 patients (467 cystocele, 158 normal). The model fused preprocessed two-dimensional (2D, resting and Valsalva) and three-dimensional (3D, levator hiatus) images as input. On the basis of a ResNet50 backbone, FD-Net performed both typing (normal, type I/II/III) and grading (normal, mild, significant) tasks. Its performance was compared against single-modal models using only 2D images (ST-Net for typing, SG-Net for grading). Evaluation metrics included accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC), with model comparisons made using McNemar tests.</p> Results <p>On the test set (<i>n</i> = 188), compared with single-modal models using only 2D images, FD-Net achieved higher accuracy in typing (79.68% vs. ST-Net’s 70.05%,&#xa0;<i>P</i> = 0.023) and grading (81.38% vs. SG-Net’s 71.28%,&#xa0;<i>P</i> = 0.006). The F1-score improved notably for normal cases (from 64.94% to 85.44%) and mild cystocele (from 57.94% to 69.47%). For other key categories, FD-Net also attained high F1-scores of 87.50% for significant prolapse and 77.08% for type III. All AUC values exceeded 0.92.</p> Conclusions <p>The dual-task model with image fusion accomplishes simultaneous cystocele typing and grading, showing higher diagnostic performance than single-modal models, and holds potential for clinical application.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Dual-Task Deep-Learning Model with Fused Ultrasound Images for Simultaneous Typing and Grading of Cystocele

  • 期刊论文

  • 发表日期：

    2026年5月8日

查看全文
  • Shiyi Ran,
  • Rong Lu,
  • Muchen Li,
  • Can Qu

摘要

Introduction and Hypothesis

We developed a dual-task deep-learning model, termed FD-Net, which utilizes fused two-dimensional (2D) and three-dimensional (3D) ultrasound images to simultaneously automate cystocele typing and grading, and evaluated its diagnostic performance.

Methods

We retrospectively included 625 patients (467 cystocele, 158 normal). The model fused preprocessed two-dimensional (2D, resting and Valsalva) and three-dimensional (3D, levator hiatus) images as input. On the basis of a ResNet50 backbone, FD-Net performed both typing (normal, type I/II/III) and grading (normal, mild, significant) tasks. Its performance was compared against single-modal models using only 2D images (ST-Net for typing, SG-Net for grading). Evaluation metrics included accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC), with model comparisons made using McNemar tests.

Results

On the test set (n = 188), compared with single-modal models using only 2D images, FD-Net achieved higher accuracy in typing (79.68% vs. ST-Net’s 70.05%, P = 0.023) and grading (81.38% vs. SG-Net’s 71.28%, P = 0.006). The F1-score improved notably for normal cases (from 64.94% to 85.44%) and mild cystocele (from 57.94% to 69.47%). For other key categories, FD-Net also attained high F1-scores of 87.50% for significant prolapse and 77.08% for type III. All AUC values exceeded 0.92.

Conclusions

The dual-task model with image fusion accomplishes simultaneous cystocele typing and grading, showing higher diagnostic performance than single-modal models, and holds potential for clinical application.