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相关概念视频

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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相关实验视频

Updated: May 9, 2026

A Reproducible Computerized Method for Quantitation of Capillary Density using Nailfold Capillaroscopy
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深度学习性能分析指甲视频毛发镜图像系统性硬化症中的图像

Müçteba Enes Yayla1, Ayhan Aydın2, Mahmut Kılıçaslan3

  • 1Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, Ankara University, Ankara 06230, Turkey.

Diagnostics (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

深度学习模型准确地分类了指甲视频毛囊镜 (NVC) 图像,用于检测系统性硬化症 (SSc). 这种AI方法显示出与专家风湿病学家诊断能力相匹配的潜力.

关键词:
人工智能的人工智能是人工智能.这是分类分类的分类.深度学习是一种深度学习.指甲折叠视频毛发显微镜系统性硬化症 系统性硬化症

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科学领域:

  • 医学成像分析 医学成像分析
  • 人工智能在医学中的应用
  • 风湿病学 诊断 诊断 风湿病学

背景情况:

  • 指甲视频毛囊镜 (NVC) 对于诊断全身性硬化症 (SSc) 是至关重要的.
  • 手动分类NVC图像可能是主观和耗时的.
  • 深度学习为自动化NVC图像分析提供了一个潜在的解决方案.

研究的目的:

  • 使用深度学习对系统性硬化症 (SSc) 患者和健康对照的NVC图像进行分类.
  • 为了比较NVC图像分类的六种不同的深度学习模型的性能.
  • 评估深度学习的诊断准确性,与专家风湿病学家对比.

主要方法:

  • 从50名SSc患者和30名健康个体的977张NVC图像的数据集进行了策划.
  • 图像被关节病学家分为正常,早期,活跃和晚期的SSc模式.
  • 六个深度学习模型 (MobileNetV3Large,ResNet152V2,Xception,VGG-19,InceptionV3,NASNetLarge) 被训练并使用准确度,精度,回忆和F1评分进行评估.

主要成果:

  • 深度学习模型实现了高准确度 (90.6%98.9%),精度 (93.4%98.9%),回忆力 (90.6%98.8%),以及F1分数 (92%98.9%).
  • 发明V3模型表现出卓越的性能,准确率为98.95%,精度为98.94%,回忆率为98.80%,F1得分为98.88%.
  • 所有模型都显示出出色的ROC AUC值 (98.99%100%),表明了强大的诊断能力.

结论:

  • 深度学习模型可以有效地对NVC图像进行系统性硬化症诊断的分类.
  • 人工智能模型的性能接近经验丰富的风湿病学家的性能.
  • 这项技术有望提高SSc诊断的效率和准确性.