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相关概念视频

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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相关实验视频

Updated: Jun 8, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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一个深度学习模型来预测乳房植入物纹理类型使用超声波图像:可行性发展研究研究.

Ho Heon Kim1, Won Chan Jeong2, Kyungran Pi3

  • 1Department of Biomedical Informatics, Medical School of Yonsei University, Seoul, Republic of Korea.

JMIR formative research
|November 5, 2024
PubMed
概括
此摘要是机器生成的。

这项研究表明,深度学习可以从超声波图像中准确地分类乳腺植入物外纹理,有助于诊断乳腺植入物相关的性大细胞淋巴瘤 (BIA-ALCL). 这种方法提供了一个可靠的替代品,用于识别植入物类型,当医疗史是不可用的.

关键词:
人工智能的人工智能是人工智能.乳房植入物是如何使用的贝表面的地形学.深度学习是一种深度学习.机器学习是机器学习.进行乳房整形术 (Mammoplasty).超声波:人工智能辅助的诊断.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 纹理乳房植入物与乳房植入物相关的形大细胞淋巴瘤 (BIA-ALCL) 有关.
  • 准确识别乳房植入物外纹理对于BIA-ALCL诊断至关重要.
  • 目前的方法,如患者召回和超声波,在纹理评估方面存在局限性.

研究的目的:

  • 评估深度学习模型的可行性,用于分类乳腺植入物外纹理.
  • 评估模型在异质超声波图像上的预测性能.
  • 建立一个强大的,定量方法来分析植入物质感.

主要方法:

  • 一个深度学习模型 (ResNet-50) 在19502张来自不同来源 (Canon,GE,公共数据集) 的乳房植入物超声图像上进行了训练.
  • 模型性能使用分层5倍交叉验证和外部数据集进行了验证.
  • 梯度加权类激活映射 (Grad-CAM) 和香农被用于像素贡献分析和预测不确定性评估.

主要成果:

  • 深度学习模型实现了高性能,AUROC值从0.909到0.985不等,PRAUC值从0.748到0.958不等.
  • 该模型保持了定量验证准确性,即使掩盖了高达90%的贡献较少的像素.
  • 预测不确定性在图像组之间有所不同,Canon (0.066) 的预测不确定性最低,而没有植入物的图像 (0.777) 的预测不确定性最高.

结论:

  • 深度学习模型可以有效地从超声波图像中预测乳房植入物外纹理.
  • 这种人工智能驱动的方法为纹理分类提供了定量方法.
  • 这些发现支持使用深度学习作为BIA-ALCL的初步诊断工具.