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基于超声波的图像分析来预测动脉狭窄风险:对问题,技术,数据集和未来方向的全面审查

Najmath Ottakath¹, Somaya Al-Maadeed¹, Susu M Zughaier²

¹Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.

Diagnostics (Basel, Switzerland)

|August 12, 2023

概括

这篇评论探讨了超声波图像分析,用于检测动脉斑块. 它涵盖了使用机器学习和深度学习进行细分,测量和分类技术,以改善心血管疾病诊断.

科学领域:

医疗成像医学成像
心血管诊断心血管诊断服务
人工智能在医学中的应用

背景情况:

动脉斑块的积累是心血管疾病的重要危险因素,包括中风.
超声波成像是评估动脉斑块的主要非侵入性诊断工具.
精确检测和表征斑块对于患者管理和风险分层至关重要.

研究的目的:

综合审查关于超声波图像分析方法的现有文献,以检测和表征动脉斑块.
分析数据集,细分技术和机器学习/深度学习方法用于斑块分析.
确定当前的挑战和该领域的未来研究方向.

主要方法:

对动脉斑块的超声波图像分析技术的文献综述.
分析图像细分方法的斑块面积,光线面积和内密介质厚度 (IMT).
检查深度学习和机器学习应用程序用于斑块测量,表征,分类和狭窄分级.

主要成果:

该综述综合了分析动脉超声图像的各种方法.
它强调了人工智能的应用,包括深度学习和机器学习,在斑块分析中.
讨论了当前方法的性能指标,挑战和局限性.

结论:

先进的图像分析技术,特别是人工智能驱动的方法,在改善动脉斑块的检测和表征方面表现有前途.
需要进一步的研究来应对当前的挑战,并提高这些方法的临床效用.
准确的斑块评估对于预防中风等心血管事件至关重要.

关键词:

美国美国美国动脉狭窄的风险动脉狭窄的风险这是分类分类的分类.计算机视觉计算机视觉深度学习是一种深度学习.机器学习是机器学习.斑块的表征斑块的表征细分化细分化的细分化

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