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

Editorial: Cancer Immunosurveillance.

Frontiers in immunology·2026
Same author

JOINclusion: A serious mobile game for promoting ethnocultural empathy in schools.

The British journal of developmental psychology·2026
Same author

Development and validation of a multiplexed targeted HILIC-HRMS assay for quantitative analysis of hepatocellular carcinoma circulating biomarkers.

Analytical and bioanalytical chemistry·2026
Same author

Light-triggered drug release via fiber optic heater-integrated with thermoresponsive microgels for locoregional cancer therapy.

Scientific reports·2026
Same author

Time series forecasting for bug resolution using machine learning and deep learning models.

Frontiers in big data·2026
Same author

All-Polymer Multilayer Lab-on-Fiber Ultrasonic Detectors in the Biomedical Field: A Numerical Study in Pursuit of Photoacoustic Applications.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: Jul 10, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

59

通过人工智能辅助拉曼光谱学评估人类初级肝癌细胞.

Concetta Esposito1,2, Mohammed Janneh1,2, Sara Spaziani1,2

  • 1Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy.

Cells
|November 24, 2023
PubMed
概括

人工智能 (AI) 与拉曼光谱学相结合可以有效地识别肝癌细胞. 这种人工智能辅助的方法在使用光谱数据将瘤细胞与非瘤细胞区分时达到近90%的准确性.

关键词:
拉曼光谱法 拉曼光谱法 拉曼光谱法肝癌细胞 肝癌细胞机器学习是机器学习.神经网络的神经网络的神经网络

更多相关视频

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
07:54

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

8.2K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.1K

相关实验视频

Last Updated: Jul 10, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:08

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

59
Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
07:54

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting

Published on: March 25, 2019

8.2K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.1K

科学领域:

  • 生物医学光谱学 生物医学光谱学
  • 人工智能在医学中的应用
  • 肝细胞癌研究 肝细胞癌研究

背景情况:

  • 准确识别肝癌细胞对于有效治疗至关重要.
  • 拉曼光谱技术提供了无标签的细胞生化指纹.
  • 整合人工智能可以增强光谱方法的分析能力.

研究的目的:

  • 评估人工智能辅助拉曼光谱技术用于肝癌细胞识别的疗效.
  • 为了区分肝细胞癌 (HCC) 瘤细胞和相邻的非瘤细胞.
  • 评估应用到拉曼光谱数据的机器学习模型的预测准确性.

主要方法:

  • 来自HCC组织的原发性肝细胞 (40种瘤,40种非瘤) 用拉曼微光谱分析.
  • 细胞的形态和光谱特征被初步评估.
  • 我们使用了包括多变量模型和神经网络在内的三种机器学习方法来分析光谱数据.

主要成果:

  • 人工智能辅助的拉曼光谱显示了分类肝癌细胞的巨大潜力.
  • 这些模型成功地根据光谱特征区分了瘤和非瘤肝细胞.
  • 对于单频谱预测细胞类型的准确度达到了近90%.

结论:

  • 人工智能增强的拉曼光谱是快速准确识别肝癌细胞的有希望的技术.
  • 这种方法为肝细胞癌诊断提供了一种高通量,无标签的方法.
  • 该研究验证了机器学习在解释复杂光谱数据的临床应用中的有效性.