Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Shared Genetic Architecture of Epilepsy and Glioma Revealed by Mendelian Randomization: Identifying Prognostic Biomarkers and Therapeutic Targets.

Current cancer drug targets·2026

Same author

Optimal timing of ileostomy closure after rectal surgery: A Bayesian network meta-analysis of randomized controlled trials.

Langenbeck's archives of surgery·2026

Same author

<b>A review of the Plecoptera of the Xinjiang Uygur Autonomous Region, China</b>.

Zootaxa·2026

Same author

Highly Efficient Complete Hydrogenation of (Polystyrene‑<i>b</i>‑Poly(styrene-<i>alt</i>-1,3-pentadiene)‑<i>b</i>‑Polystyrene) Triblock Copolymer Using a Low-Cost Ni-Based Catalyst.

ACS omega·2026

Same author

Identification of genes controlling grain size in barley through RNA-seq and weighted gene co-expression network analysis.

Functional & integrative genomics·2026

Same author

REPLY.

Diseases of the colon and rectum·2026

Search research articles

相关实验视频

Updated: Jan 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

一个可解释的深度学习模型用于使用多参数MRI进行焦点肝损伤诊断.

Zhehan Shen^1,2, Lingzhi Chen³, Lilong Wang³

¹Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin 2nd Rd, Huangpu District, Shanghai 200025, China.

Radiology. Artificial intelligence

|September 10, 2025

概括

使用多参数MRI的新型深度学习模型显著提高了放射科医生在分类焦点肝病变 (FLLs) 的准确性和效率. 这种人工智能工具提高了诊断性能,特别是对于初级放射科医生.

关键词:

应用程序域名应用程序域名卷积神经网络 (CNN) 是一种神经网络.深度学习算法深度学习算法功能检测功能检测功能检测肝脏肝脏肝脏肝脏增强了MR-动态对比度的增强对比度机器学习算法机器学习算法视觉视觉视觉视觉视觉是一个

更多相关视频

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

相关实验视频

Last Updated: Jan 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

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
机器学习用于医疗保健

背景情况:

焦点性肝病变 (FLLs) 需要准确的分类,以便有效的患者管理.
多参数MRI为FLL表征提供了丰富的数据.
深度学习为复杂的医学成像数据的自动分析提供了潜力.

研究的目的:

通过使用多参数MRI特征来评估FLL分类的可解释深度学习模型.
评估模型对放射科医生的诊断准确性和效率的影响.

主要方法:

开发nn-Unet用于细分和肝脏成像特征变压器,用于从多参数MRI进行FLL (≥1厘米) 的分类.
跨多个机构的追溯和前性验证.
模型辅助放射科医生的性能与无辅助读数的比较.

主要成果:

在测试组中具有高的细分精度 (Dice:0.98用于肝脏,0.96用于瘤) 和分类精度 (93-97%).
模型辅助导致初级放射科医生诊断准确度增加5.3% (P < .001).
辅助阅读减少了34.5秒的阅读时间 (P < .001) 和增加了信心 (P < .001).

结论:

可解释的深度学习模型在检测和分类FLL方面表现出高准确性.
人工智能辅助口译显著提高了放射科医生的诊断准确性和效率,特别是初级放射科医生.
这种方法有望改善临床实践中的FLL诊断.