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

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

722
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
722

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

Corrigendum to "Beta-hydroxybutyrate counteracts the deleterious effects of a saturated high-fat diet on synaptic AMPAR receptors and cognitive performance" [Mol Metabol (2025) 102207].

Molecular metabolism·2026
Same author

Acute temperature effects on cilia beating increase coral deoxygenation.

Science advances·2026
Same author

Fenofibrate targets PPARα-CPT1C axis to reverse aging by regulating lipid metabolism and mitochondrial function.

Pharmacological research·2026
Same author

Objective Assessment of Attention Deficit Hyperactivity Disorder with QbMobile: A Smartphone Application for Clinical Use.

Clinical practice and epidemiology in mental health : CP & EMH·2026
Same author

Correction: Machine learning on a smartphone-based CPT for ADHD prediction.

Frontiers in psychiatry·2026
Same author

First-in-class SAM-competitive G9a inhibitor FLAV-27 as a disease-modifying therapy for Alzheimer disease.

Molecular therapy : the journal of the American Society of Gene Therapy·2025

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.2K

基于智能手机的CPT上的机器学习用于ADHD预测.

Núria Casals1, Simon Larsson1, Mikkel Hansen1

  • 1Medical Department, Qbtech AB, Stockholm, Sweden.

Frontiers in psychiatry
|November 24, 2025
PubMed
概括
此摘要是机器生成的。

智能手机传感器和持续性能测试 (CPT) 可以准确诊断注意力缺陷/多动障碍 (ADHD). 整合CPT,面部和运动数据的机器学习模型显示出高诊断性能,超过了传统方法.

关键词:
更多关于 ADHD ADHD 的文章在这里,我们可以看到AIAIAI.在 CPT CPT 中使用.面部跟踪系统 面部跟踪系统机器学习是机器学习.移动移动移动移动移动运动传感器的运动传感器.智能手机的智能手机智能手机的智能手机.

更多相关视频

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
10:02

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

16.6K
Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
13:09

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

10.8K

相关实验视频

Last Updated: Jan 10, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.2K
Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
10:02

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

16.6K
Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
13:09

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

10.8K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 精神病学是一个精神病学.

背景情况:

  • 持续性表现测试 (CPT) 是注意力缺陷/多动障碍 (ADHD) 评估的标准.
  • 智能手机传感器为心理健康提供了新的行为监测.
  • 机器学习 (ML) 正在成为提高ADHD诊断的工具.

研究的目的:

  • 评估使用智能手机进行基于CPT的ADHD评估的可行性.
  • 确定智能手机传感器数据在预测ADHD诊断中的实用性.
  • 开发和验证使用智能手机数据进行ADHD评估的ML模型.

主要方法:

  • 利用了952名神经类型个体和292名非药物治疗的多动症患者的数据.
  • 开发了一种基于人口统计,CPT,面部和运动传感器数据的ML模型.
  • 顺序添加特征组来评估ADHD的预测性能.

主要成果:

  • 最好的ML模型实现了0.808的灵敏度和0.799.79的PR-AUC.
  • 整合智能手机传感器数据显著改善了诊断性能.
  • 性能在不同年龄组 (6-60岁) 或性别之间没有显著差异.

结论:

  • 基于智能手机的CPT可以准确评估ADHD.
  • 整合智能手机传感器数据 (面部跟踪,运动) 可以提高诊断准确度.
  • 这种方法显示出超越传统的基于计算机的ADHD评估的潜力.