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Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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基于深度学习的乳腺瘤分类使用剪波电磁共振图像特征和临床变量.

Mohammad-Bagher Shiran1, Sepideh Abdollahi-Dehkordi2, Arash Zare-Sadeghi1

  • 1Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran.

Advanced biomedical research
|October 24, 2025
PubMed
概括

临床变量显著提高卷积神经网络 (CNN) 在使用剪波弹性图像 (SWE) 分类乳腺质量的性能. 这种方法提高了乳腺病变的诊断准确性.

关键词:
乳腺癌 乳腺癌 乳腺癌这是分类分类的分类.临床变量 临床变量卷积神经网络是一种卷积神经网络.弹性学弹性学 弹性学

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 在瘤学瘤学.

背景情况:

  • 剪波弹性图 (SWE) 有助于早期和高效的乳腺质量分类.
  • 卷积神经网络 (CNN) 在分析医疗图像方面表现有前途.
  • 将临床数据与成像功能相结合,有可能提高诊断准确度.

研究的目的:

  • 评估临床变量和SWE图像特征在乳腺质量分类中的作用.
  • 在此分类任务中评估各种CNN模型 (ResNet101,VGG16,Exception,InceptionV3,DenseNet169) 的性能.
  • 为了确定兴趣区域 (ROI) 选择对分类性能的影响.

主要方法:

  • 未来收集了834张SWE图像,534张用于培训CNN.
  • 接受CNN模型的训练,仅使用图像特征,并与临床变量相结合.
  • 对B模式图像进行手动ROI选择和不进行手动ROI选择的分类性能评估.

主要成果:

  • 在使用临床变量和SWE图像特征时,DenseNet169和ResNet152实现了最高的性能.
  • DenseNet169获得了94.01%的准确性和AUC值为0.86 (测试) 和0.97 (验证).
  • 结合临床变量和图像特征,显著改善了DenseNet169,VGG16和InceptionV3的AUC,并选择了ROI (P ≤0.05).

结论:

  • 临床变量显著提高大多数CNN在SWE图像上的乳腺质量分类与ROI选择的性能.
  • 将临床数据与SWE成像和CNN分析相结合,为乳腺病变的表征提供了更强大的方法.
  • 当获得全面数据时,CNN模型,特别是DenseNet169和ResNet152,在分类乳腺质量方面表现出高效率.