使用神经网络进行组织学和尿路蛋白质学的计算整合
Nicholas Lucarelli1, Donghwan Yun2, Dohyun Han3
1Department of Pathology and Anatomical Sciences, University at Buffalo - The State University of New York, Buffalo, New York.
概括
人工智能整合了脏活检图像与尿液蛋白质基因数据,以找到疾病联系. 组织形态学特征,如RGB颜色和PAS+区域变异,预测分子概况,有助于理解病进展.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 脏活检和分子谱对于诊断脏疾病至关重要.
- 分子数据可以提供超越传统遗传病理学的见解.
- 整合多模式数据可以提高对病的理解.
研究的目的:
- 通过人工智能计算将尿蛋白质样本与脏活检微观结构形态联系起来.
- 确定与糖尿病病 (DN) 进展到末期病 (ESRD) 相关的分子和图像特征.
- 发现病中图像表型的新型分子联系.
主要方法:
- 分析了来自56名DN患者的周期性酸-Schiff染色脏活检的整片图像.
- 泌尿蛋白质组概况 (2,038种蛋白质) 与组织学数据相匹配.
- 由人工智能驱动的分析将组织形态学特征 (球粒和管) 与蛋白质表达相结合,以预测疾病进展.
- 经过训练,完全连接的网络将图像特征与差异表达的尿蛋白相关联.
主要成果:
- 人工智能成功地将组织学图像特征与尿路蛋白质基因数据集成在一起.
- 特定的图像特征,包括RGB颜色值和PAS阳性区域的方差,对分子概况具有很高的预测性.
- 确定了与DN向ESRD进展相关的显著图像特征-蛋白质对.
- 发现基因组形状特征是与疾病进展相关的分子概况的显著预测因素.
结论:
- 在病中,分子形状和图像表型之间存在着强烈的联系,通过计算方法阐明.
- 发现的联系为疾病途径和病发病率和进展的潜在新因素提供了洞察力.
- 人工智能驱动的组织学和分子数据的整合为推进脏学研究提供了强大的方法.
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