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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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相关实验视频

Updated: Jun 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

基于视觉变压器的深度学习模型,用于使用MRI进行全膝关节置换预测.

Chaojie Zhang¹, Shengjia Chen¹, Ozkan Cigdem¹

¹Department of Radiology, New York University Grossman School of Medicine, 227 E 30th St, 7th Fl, New York, NY 10016.

Radiology. Artificial intelligence

|July 16, 2025

概括

一个新的深度学习模型,MR-Transformer,使用MRI扫描准确预测膝关节骨关节炎进展到膝关节完全置换. 这种先进的模型与现有的膝盖核磁共振分析方法相比,显示出更高的性能.

关键词:

膝盖膝盖膝盖膝盖在这就是为什么MRI是MRI.预测预后预测预测监督学习监督学习

更多相关视频

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

相关实验视频

Last Updated: Jun 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
整形外科和肌肉骨疾病

背景情况:

膝关节关节炎 (OA) 是导致残疾的主要原因,通常需要全膝关节置换 (TKR).
准确预测关节炎的进展对于及时干预和改善患者结果至关重要.
当前的预测模型往往缺乏足够的复杂性来充分利用复杂的MRI数据.

研究的目的:

开发和评估MR-Transformer,这是一个新的深度学习模型,用于预测膝盖OA进展到TKR.
利用ImageNet预训练和3D空间相关性来提高预测准确度.
将MR-Transformer的性能与多个MRI序列的最先进的深度学习模型进行比较.

主要方法:

353个骨关节炎倡议 (OAI) 和270个多中心骨关节炎研究 (MOST) 膝盖MRI数据集的回顾性分析.
包括四个MRI序列:COR-IW-TSE,SAG-IW-TSE-FS,COR-STIR和SAG-PD-FAT-SAT. 这四个MRI序列包括在内.
七重嵌套交叉验证以评估MR-Transformer与TSE-Net,3DMeT和MRNet之间的关系.

主要成果:

在所有MRI序列 (0.84-0.88) 中,MR-Transformer在接收器操作特征曲线 (AUCs) 下实现了高面积.
该模型在所有序列中 (P < .001) 与3DMeT相比显示出更高的AUC.
对COR-IW-TSE序列观察到最高的灵敏度 (83%) 和特异性 (83%).

结论:

使用MRI,MR-Transformer在预测膝关节OA进展到TKR方面表现出最先进的性能.
该模型能够集成ImageNet预训练和3D空间信息,从而提高预测能力.
MR-Transformer代表了人工智能驱动的膝关节骨关节炎预后工具的重大进步.