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

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

3.1K
Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
3.1K

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

Validation of Azure Kinect for Upper Limb Motion Analysis Under Optimal and Suboptimal Conditions.

Sensors (Basel, Switzerland)·2026
Same author

Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.

NPJ precision oncology·2026
Same author

Beyond the calendar: A narrative review on chronobiological drivers of prognosis in resectable NSCLC.

Tumori·2026
Same author

Redefining therapeutic horizons in thymic epithelial tumors: precision, combinations, and emerging biology.

NPJ precision oncology·2026
Same author

Alectinib and gastrointestinal perforation in ALK-rearranged non-small cell lung cancer: A case series.

Tumori·2026
Same author

Adjuvant chemotherapy in stage I triple-negative breast cancer: A systematic review and meta-analysis of survival outcomes.

Cancer treatment reviews·2026

相关实验视频

Updated: Jan 9, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

406

自动咳分析用于检测非小细胞肺癌.

Chiara Giangregorio, Cristina Maria Licciardello, Vanja Miskovic

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    自动咳分析显示了早期非小细胞肺癌 (NSCLC) 检测的前景. 机器学习模型,特别是CNN,可以区分NSCLC患者和健康个体,有助于肺癌查.

    更多相关视频

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    885
    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
    08:05

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

    Published on: December 19, 2020

    14.7K

    相关实验视频

    Last Updated: Jan 9, 2026

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    406
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    885
    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
    08:05

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

    Published on: December 19, 2020

    14.7K

    科学领域:

    • 生物医学工程 生物医学工程
    • 人工智能在医学中的应用
    • 呼吸系统医学 呼吸系统医学

    背景情况:

    • 早期发现非小细胞肺癌 (NSCLC) 对于改善患者存活率至关重要.
    • 需要新的非侵入性查工具来促进早期NSCLC诊断.
    • 自动咳分析为预先查肺癌提供了潜在的途径.

    研究的目的:

    • 研究使用机器学习自动咳分析的有效性,以区分NSCLC患者与健康对照.
    • 为了比较各种机器学习算法的性能,包括SVM,XGBoost和CNNs,用于基于咳的NSCLC检测.
    • 评估跨人口群体的模型解释性和公平性.

    主要方法:

    • 从227名受试者 (NSCLC患者和健康对照) 获得咳录音.
    • 应用机器学习技术:支持矢量机器 (SVM),XGBoost,卷积神经网络 (CNN) 和转移学习 (VGG16).
    • 使用Shapley添加式解释 (SHAP) 进行模型解释性和评估公平性,使用年龄和性别之间的均等赔率差异.

    主要成果:

    • 卷积神经网络 (CNN) 在测试组中获得了最高的精度 (0.83).
    • 支持矢量机 (SVM) 展示了具有竞争力的性能 (0.78精度) 和适合低资源设置.
    • SHAP分析提高了SVM模型的透明度;公平性分析表明年龄和性别之间存在较小的差异.

    结论:

    • 由机器学习驱动的自动咳分析显示出作为NSCLC预先查的非侵入性工具的巨大潜力.
    • CNNs提供卓越的性能,而SVM提供了一个可行的替代方案,具有增强的可解释性.
    • 需要对更大,更多样化的数据集进行进一步的研究,以验证和完善这些发现,以便临床应用.