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Peripheral Artery Disease IV: Nursing Management01:26

Peripheral Artery Disease IV: Nursing Management

314
 The nursing management of a patient with peripheral artery disease (PAD) begins with a thorough assessment of the patient’s health history and clinical manifestations.AssessmentHealth History: Evaluate the patient’s history of hypertension, hyperlipidemia, family history of cardiovascular issues, and lifestyle factors such as dietary patterns, smoking, and physical activity.Physical Examination:Assess the affected extremity for decreased or absent peripheral pulses,...
314
Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

317
Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
317

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相关实验视频

Updated: Jan 7, 2026

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model
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使用深度学习和机器学习进行基于图像的糖尿病足诊断的进展:系统性审查.

Haifa F Alhasson1, Shuaa S Alharbi1

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.

Biomedicines
|December 30, 2025
PubMed
概括

机器学习 (ML) 和深度学习 (DL) 显示出使用图像诊断糖尿病足 (DFU) 的前景. 改进数据共享对于提高准确性和患者护理至关重要.

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 糖尿病足部的诊断 糖尿病足部的诊断

背景情况:

  • 糖尿病足 (DFU) 是糖尿病的一个严重并发症,需要先进的诊断工具.
  • 目前对DFU的诊断方法可能是主观的,耗时的.
  • 机器学习 (ML) 和深度学习 (DL) 的整合为客观和高效的DFU诊断提供了潜力.

研究的目的:

  • 系统地审查机器学习 (ML) 和深度学习 (DL) 在基于图像的糖尿病足 (DFU) 诊断中的应用.
  • 确定ML/DL研究中的趋势,挑战和质量指标,用于DFU检测,细分和分类.
  • 评估当前ML/DL方法在DFU诊断中的有效性和局限性.

主要方法:

  • 在14个数据库中对2010年至2025年间发表的研究进行了全面的系统文献搜索.
  • 纳入标准侧重于ML/DL基于图像的研究,用于DFU诊断,并提供定量结果.
  • 在详细审查后,从最初的4653篇文章中选择了102项研究.

主要成果:

  • ML/DL模型显示了DFU的高诊断性能,准确度从0.88到0.97.
  • 通常使用的ML技术包括支持矢量机器 (SVM),而U-Net和完全卷积神经网络 (FCNNs) 在DL中普遍存在.
关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.dfu 数据集 dfu 数据集在糖尿病中,糖尿病是血糖性糖尿病.糖尿病人的足部.机器学习是机器学习.一个热量图 (thermogram) 是一个温度图.

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  • 热红外成像已经成为一个有前途的模式,但数据集的有限的公共可访问性 (45%用于细分,67.3%用于分类) 阻碍了可重复性.

    • 结论:

    • ML和DL是DFU诊断的有效工具,提供高精度和灵敏度.
    • 审查强调了增加数据可用性和共享的关键需求,以加强ML/DL模型的可复制性和可靠性.
    • 提高数据可访问性最终将有助于提高诊断准确性和改善DFU管理中的患者结果.