Scholarlink

<p>To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional Networks (ResNet + FCN) segmentation model were trained. The better effective model was selected to appraise the classification performance of 3 breast radiologists and 3 non-breast radiologists. The evaluation metrics included sensitivity, specificity, and positive predictive value (PPV). The ResNet101 model demonstrated superior performance compared to the ResNet + FCN model. It significantly enhanced the classification sensitivity of all radiologists by 0.060, 0.021, 0.170, 0.009, 0.052, and 0.047, respectively. For P1 to P4 glandular, the PPVs of all radiologists increased by 0.154, 0.178, 0.027, and 0.109 with Ai-assisted. Notably, the non-breast radiologists experienced a particularly substantial rise in PPV (<i>p</i> &lt; 0.01). This study trained ResNet 101 deep learning model is a reliable and accurate system for assisting different experienced radiologists differentiate dense breast glandular tissue components in ultrasound images.</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

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月6日

查看全文
  • Hongju Yan,
  • Chaochao Dai,
  • Xiaojing Xu,
  • Yuxuan Qiu,
  • Lifang Yu,
  • Lewen Huang,
  • Bei Lin,
  • Jianan Huang,
  • Chenxiang Jiang,
  • Yingzhao Shen,
  • Jing Ji,
  • Youcheng Li,
  • Lingyun Bao

摘要

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional Networks (ResNet + FCN) segmentation model were trained. The better effective model was selected to appraise the classification performance of 3 breast radiologists and 3 non-breast radiologists. The evaluation metrics included sensitivity, specificity, and positive predictive value (PPV). The ResNet101 model demonstrated superior performance compared to the ResNet + FCN model. It significantly enhanced the classification sensitivity of all radiologists by 0.060, 0.021, 0.170, 0.009, 0.052, and 0.047, respectively. For P1 to P4 glandular, the PPVs of all radiologists increased by 0.154, 0.178, 0.027, and 0.109 with Ai-assisted. Notably, the non-breast radiologists experienced a particularly substantial rise in PPV (p < 0.01). This study trained ResNet 101 deep learning model is a reliable and accurate system for assisting different experienced radiologists differentiate dense breast glandular tissue components in ultrasound images.