相关实验视频

Updated: May 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

多器官系统性硬化症轨迹的深层次分类 - - 欧星研究

Cécile Trottet^1,2, Manuel Schürch^3,4, Ahmed Allam¹

¹Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

NPJ digital medicine

|September 1, 2025

概括

使用深度学习识别了系统性硬化 (SSc) 的亚型. 一个新的分类揭示了五个患者群,改善了器官并发症的风险预测,特别是在有限的皮肤病例中.

科学领域:

免疫学
关节病学
计算生物学

背景情况:

系统性硬化 (SSc) 是一种复杂的自身免疫性疾病,具有多器官损伤的特征.
目前的SSc分类依赖于皮肤参与 (有限与扩散),但器官特定的变异性表明存在更多的亚型.

研究的目的:

开发一个半监督的深度学习框架,用于SSc患者分层.
根据器官参与和严重程度确定新的临床意义上的SSc亚型.

主要方法:

对欧洲硬质皮肤试验和研究 (EUSTAR) 数据库 (14,000名患者,67,000次访问) 应用了生成深度学习模型.
专家定义的器官参与标准被用来模拟疾病的发展轨迹.
系统评估模型性能,对缺失数据的稳定性和临床解释性.

主要成果:

根据器官受损程度确定了五个不同的患者群.
一组皮肤SSc有限的患者表现出肺部和心脏并发症的显著风险.
基于数据的方法确定了超越传统分类的临床相关患者分层.

结论:

深度学习模型可以通过整合多器官数据来发现新的SSc亚型.
这种方法提高了患者的分层和预后,特别是在识别高风险个体时.
多器官建模对于补充SSc管理中的临床见解至关重要.

更多相关视频

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

相关概念视频

Multiple Sclerosis l: Introduction

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...