Scholarlink

<p>Biliary atresia (BA) is a rare and severe congenital disorder with a significant challenge for prenatal diagnosis. This study, registered at the Chinese Clinical Trial Registry (ChiCTR2200059705), aimed to develop an intelligent model to aid in the prenatal diagnosis of BA. To develop and evaluate this model, fetuses from 20 hospitals across China and infants sourced from public database were collected. The transfer-learning model (TLM) demonstrated superior diagnostic performance compared to the basic deep-learning model, with higher area under the curves of 0.906 (95%CI: 0.872–0.940) vs 0.793 (0.743–0.843), 0.914 (0.875–0.953) vs 0.790 (0.727–0.853), and 0.907 (0.869–0.945) vs 0.880 (0.838–0.922) for the three independent test cohorts. Furthermore, when aided by the TLM, diagnostic accuracy surpassed that of individual sonologists alone. The TLM achieved satisfactory performance in predicting fetal BA, providing a low-cost, easily accessible, and accurate diagnostic tool for this condition, making it an effective aid in clinical practice.</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

Transfer learning method for prenatal ultrasound diagnosis of biliary atresia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月28日

查看全文
  • Fujiao He,
  • Gang Li,
  • Zhichao Zhang,
  • Chaoran Yang,
  • Zeyu Yang,
  • Hao Ding,
  • Dan Zhao,
  • Wei Sun,
  • Yu Wang,
  • Kaihui Zeng,
  • Xian Li,
  • Mingming Shao,
  • Jiao Yin,
  • Jia Yao,
  • Boxuan Hong,
  • Zhibo Zhang,
  • Zhengwei Yuan,
  • Zongjie Weng,
  • Luyao Zhou,
  • Mo Zhang,
  • Lizhu Chen

摘要

Biliary atresia (BA) is a rare and severe congenital disorder with a significant challenge for prenatal diagnosis. This study, registered at the Chinese Clinical Trial Registry (ChiCTR2200059705), aimed to develop an intelligent model to aid in the prenatal diagnosis of BA. To develop and evaluate this model, fetuses from 20 hospitals across China and infants sourced from public database were collected. The transfer-learning model (TLM) demonstrated superior diagnostic performance compared to the basic deep-learning model, with higher area under the curves of 0.906 (95%CI: 0.872–0.940) vs 0.793 (0.743–0.843), 0.914 (0.875–0.953) vs 0.790 (0.727–0.853), and 0.907 (0.869–0.945) vs 0.880 (0.838–0.922) for the three independent test cohorts. Furthermore, when aided by the TLM, diagnostic accuracy surpassed that of individual sonologists alone. The TLM achieved satisfactory performance in predicting fetal BA, providing a low-cost, easily accessible, and accurate diagnostic tool for this condition, making it an effective aid in clinical practice.