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Renewal of Intestinal Stem Cells01:23

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The intestinal epithelial lining rapidly renews every 4 to 5 days. The renewal is facilitated by intestinal stem cells (ISCs) located at the base of the crypt– a gland located at the bottom of each villus. ISCs divide asymmetrically to form new stem cells and progenitor daughter cells. The daughter cells are called transit-amplifying (TA) cells which move upwards along the crypt and either differentiate into absorptive cells– the enterocytes or secretory cells– including the...
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Multimodal Quantitative Phase Imaging with Digital Holographic Microscopy Accurately Assesses Intestinal Inflammation and Epithelial Wound Healing
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开发一种深度学习算法,用于对炎症性肠病的Paneth细胞密度量化.

Liang-I Kang1, Kathryn Sarullo1, Jon N Marsh1

  • 1Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, Campus Box 8118, Saint Louis, MO, 63110, United States.

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概括

深度学习准确地量化了皮质组织中的帕内斯细胞 (PC) 密度,将其确定为克罗恩病复发的预测生物标志物. 这个人工智能工具简化了临床应用的分析.

关键词:
克罗恩氏病 克罗恩氏病是什么?病理学 病理学 病理学预测 预后 预测 预测整个幻灯片图像的图像.

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科学领域:

  • 胃肠病学 胃肠病学
  • 计算病理学计算病理学
  • 生物标志物发现发现

背景情况:

  • 皮质帕内斯细胞 (PC) 密度的改变与诸如克罗恩病 (CD) 这样的炎症性肠道疾病有关.
  • 量化PC密度对于疾病预后至关重要,但目前是耗时的,阻碍了临床工作流程.
  • 深度学习 (DL) 为准确和高效的图像评估提供了一个潜在的解决方案.

研究的目的:

  • 开发和验证一种基于DL的工具,用于量化叶组织中的PC密度.
  • 评估DL量化的PC密度作为CD复发的预测生物标志物的有用性.

主要方法:

  • 一个U-net双阶段DL模型被训练在病理学家注释的整体幻灯片图像 (WSI) 的阴茎组织.
  • DL模型量化了PC数,密码数和PC密度.
  • 专家病理学家使用RMSE和r2指标对手工量化验证了模型性能.
  • 在CD和无CD患者队列中分析了PC密度,并评估了其与CD复发的关联.

主要成果:

  • 与单阶段模型相比,双阶段DL模型实现了更高的准确性 (RMSE = 0.802,r2 = 0.748).
  • DL算法在验证中表现良好 (RMSE = 1.148,r2 = 0.708) 与专家病理学家相比.
  • 与非IBD对照组相比,在CD患者中观察到显著较低的PC密度 (2.99对比4.04PC/crypt).
  • 在PC密度最低四分位数中的CD患者表现出明显较短的无复发间隔 (p=0.0399).

结论:

  • 开发的DL模型可用于测量PC密度作为预测生物标志物.
  • 这种人工智能驱动的方法有可能增强未来的临床实践,用于管理炎症性肠道疾病.