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在用嵌入深度图像和视觉语言模型的连接使用状腺超声波识别动脉段

Chung-Ming Lo¹, Sheng-Feng Sung^2,3

¹Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan.

Physics in medicine and biology

|May 14, 2025

概括

一个人工智能模型在超声波图像中准确地对动脉段进行分类,改善了中风风险评估. 这种自动化方法提高了动脉检查的一致性和精度,以获得更好的患者结果.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管疾病心血管疾病

背景情况:

大动脉动脉样硬化是缺血性中风的关键预测因素.
动脉超声波是一种非侵入性,一线诊断工具.
准确量化动脉片段对于风险评估至关重要.

研究的目的:

开发一个自动化分类模型,用于常见动脉 (CCA),动脉球泡,内动脉 (ICA) 和外动脉 (ECA).
通过深度学习提高动脉检查的准确性和一致性.
为了提高B模式超声波中阴道动脉结构的量化.

主要方法:

从288名患者收集了2943张B模式超声波图像.
使用DenseNet201,视觉转换器和回声对比的语言图像预训练模型提取的功能.
在支持矢量机器分类器中的组合特征用于解剖结构解释.

主要成果:

在经过十倍的交叉验证后,获得了82.3%的整体分类准确度.
与单个特征集相比,表现显著改善了性能 (p < 0.001).
该模型有效地解释了B模式超声波图像中的解剖结构.

结论:

拟议的自动化模型提高了动脉检查的准确性和一致性.
这种人工智能驱动的方法有助于更好地预测和预防缺血性中风.
开发的模型为评估心血管疾病提供了有价值的工具.

关键词:

人工智能是一种人工智能.卷积神经网络是一种卷积神经网络.部超声波部超声波部超声波视觉变压器视觉变压器视觉语言模型的模型.

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