Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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

Differences of PPARD Expression in the Liver of Cattle with Different Marbling Grades.

Animals : an open access journal from MDPI·2026
Same author

Bionic Magnetic Nanorobots Combined with AI-Assisted Microscopic Imaging for Argonaute-Powered Multiplexed Foodborne Pathogens Detection.

Journal of agricultural and food chemistry·2026
Same author

Artificial intelligence-assisted microsphere immunoassay for ultrasensitive Salmonella detection through microfluidic translocation event counting.

Biosensors & bioelectronics·2026
Same author

Corrigendum to "Reduced skeletal muscle index during follow-up as a mortality risk factor in maintenance hemodialysis patients with end-stage renal disease". [Eur. J. Radiol. 200 (2026) 112857].

European journal of radiology·2026
Same author

Single-cell RNA sequencing reveals immune-peritubular myoid cell crosstalk driving testicular interstitial fibrosis in idiopathic non-obstructive azoospermia.

Cell & bioscience·2026
Same author

Multiplexed and Ultrasensitive Pathogens Detection with an Orthogonal Argonaute Circuit Based on Encoded Microsphere Microscopic Imaging.

Analytical chemistry·2026

相关实验视频

Updated: Jun 1, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

947

基于内核表示的端到端网络启用解码策略,用于准确和医学诊断.

Qinyu Wang1, Xuewen Peng2, Niu Feng2

  • 1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China.

Journal of hazardous materials
|January 17, 2025
PubMed
概括

一个新的人工智能 (AI) 模型CellNet准确地检测到成像生物传感器中的密集和粘附的目标. 这种人工智能的进步改善了用于疾病诊断和其他应用的生物标志物量化.

关键词:
人工智能的人工智能是人工智能.在体外诊断诊断.核心表示 核心表示显微镜成像技术的成像甲素是一种甲素.

更多相关视频

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

相关实验视频

Last Updated: Jun 1, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

947
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

科学领域:

  • 生物医学工程 生物医学工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 影像生物传感器为生物标志物定量提供灵活的数字分析.
  • 深度学习面临的挑战是目标密度和生物标志物检测的坚持.
  • 精确识别密集和粘附的目标对于生物传感应用至关重要.

研究的目的:

  • 介绍CellNet,一种用于检测成像中的密集目标的新型神经网络.
  • 开发一种人工智能转码生物传感方法 (bs-SMART) 用于生物标志物检测.
  • 为了验证CellNet在识别不规则和附着细胞方面的性能.

主要方法:

  • 开发了CellNet,这是一个神经网络,用于对象内核表示的形状感知辐射基函数.
  • 采用人工智能转码 (bs-SMART) 实现了一种基于生物-斯特雷普塔维丁的生物感知方法.
  • 应用CellNet检测血清样本中的prokalcitonin并评估细胞识别.

主要成果:

  • 在检测粘附的聚烯微球时,CellNet获得了98.39%的准确性.
  • 该 bs-SMART 方法证明了高精度和敏感性,用于检测甲 (LOD = 8.5 pg/mL).
  • 细胞网成功地识别了不规则的和粘附的细胞,验证了它的强度.

结论:

  • 细胞网提高了目标计数的准确性,并解决了密集目标检测的挑战.
  • bs-SMART技术为准确的疾病诊断提供了一个可靠的平台.
  • 细胞网络显示出在推进医学诊断,食品安全和环境监测方面的巨大潜力.