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

Bipolar Disorder01:30

Bipolar Disorder

152
Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
152

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

Response to correspondence: "Reappraising the neurovascular link between arterial stiffness and neuropathy: proposal for a neurovascular uncoupling index".

Journal of hypertension·2026
Same author

The Association Between Adherence to the Dutch Healthy Diet Index and Glaucoma Prevalence-The Maastricht Study.

Nutrients·2026
Same author

Association of tear fluid glutathione synthetase and glutathione levels with amyloid positivity.

Scientific reports·2026
Same author

Investigating the impact of multinational collaborations on cultural understanding, health disparities, biomedical innovations, and professional development through project-based learning.

Journal of biological engineering·2026
Same author

Explainable transformer framework for fast cotton leaf diagnostics and fabric defect detection.

iScience·2026
Same author

Uncovering the Genetic Architecture of Optic Nerve Integrity Estimates through Genome-wide Association Study Meta-analyses.

medRxiv : the preprint server for health sciences·2026

相关实验视频

Updated: Sep 18, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.8K

一项多中心研究检查了基于深度学习的计算机模型,用于使用视网膜血管图像对双相情感障碍进行分类.

Vaishak Harish1, Anantha Padmanabha2, Abhishek Appaji1

  • 1B.M.S. College of Engineering, Bengaluru, India.

Journal of affective disorders
|June 24, 2025
PubMed
概括

对视网膜图像的深度学习分析准确地将双相情感障碍患者与健康个体区分开来. 这种新的方法对精神病学中的临床诊断工具有希望.

关键词:
人工智能的人工智能是人工智能.生物标志物生物标志物计算精神病学是一种计算精神病学.卷积神经网络是一种卷积神经网络.基金基金基金基金基金基金基金

更多相关视频

Author Spotlight: Using the Split Retina Technique for Enhanced Access and Accelerated Experiments
07:53

Author Spotlight: Using the Split Retina Technique for Enhanced Access and Accelerated Experiments

Published on: January 16, 2024

4.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

相关实验视频

Last Updated: Sep 18, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.8K
Author Spotlight: Using the Split Retina Technique for Enhanced Access and Accelerated Experiments
07:53

Author Spotlight: Using the Split Retina Technique for Enhanced Access and Accelerated Experiments

Published on: January 16, 2024

4.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

科学领域:

  • 眼科和精神病学 眼科和精神病学
  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析

背景情况:

  • 视网膜充当"进入大脑的窗口",血管异常与双相情感障碍 (BD) 有关.
  • 深度学习 (DL) 提供了先进的计算方法来分析医疗图像,包括视网膜血管.
  • 以前的研究没有使用DL来使用视网膜血管图像来分类双极性障碍.

研究的目的:

  • 通过使用视网膜底图像,研究深度学习模型在分类双相情感障碍 (BD) 患者与健康志愿者 (HV) 中的有效性.
  • 在独立的测试数据集上评估开发模型的转移学习能力.

主要方法:

  • 研究了一组383名参与者 (188名BD,195名HV),数据分为培训 (327) 和测试 (56) 组.
  • 视网膜底部图像是使用非脑膜底部摄像机拍摄的.
  • 训练了一个优化的卷积神经网络 (CNN) 模型,并评估了其转移学习性能.

主要成果:

  • 在训练数据集上,CNN模型表现出高性能:88.0%的灵敏度,85.7%的特异性.
  • 在独立测试数据集上,该模型实现了85.7%的准确性,83.3%的灵敏度和92.9%的特异性.
  • 均衡的模型参数和测试组的强性能表明了强度.

结论:

  • 开发的CNN模型有效地区分了双相情感障碍患者和健康个体.
  • 这项研究强调了用于精神病诊断的视网膜图像深度学习分析的潜在临床实用性.
  • 在独立测试数据集中成功复制支持模型的转移学习能力.