大动脉动脉瘤诊断的新方向:生物标志物和机器学习
Kyle C Alexander1, John S Ikonomidis1, Adam W Akerman1
1Department of Surgery, Division of Cardiothoracic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Journal of clinical medicine
|February 10, 2024
概括
包括机器学习 (ML) 和人工智能在内的开创性技术将改变大动脉动脉瘤诊断和管理. 本综述探讨了ML诊断,生物标志物和它们的融合,以改善临床工具.
科学领域:
- 心血管医学 心血管医学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 大动脉动脉瘤疾病,特别是胸腔动脉瘤,带来了重大的诊断和管理挑战.
- 现有的诊断方法需要改进,以提高准确性和效率.
研究的目的:
- 审查和评估用于大动脉动脉瘤诊断和管理的新兴技术.
- 探索机器学习 (ML),计算机辅助诊断和生物标志物在促进大动脉疾病评估中的作用.
主要方法:
- 关于大动脉动脉瘤诊断的当代和历史研究的综合文献调查.
- 深入分析ML算法和计算机辅助诊断概念.
- 从先前的研究和PubMed中央数据库中对循环生物标志物的结合.
主要成果:
- 机器学习和人工智能为大动脉动脉瘤诊断和管理带来革命性的巨大潜力.
- 人工智能引导测量和ML框架与生物标志物分析的整合显示出有希望.
- 目前的研究强调了将AI转化为临床实践的挑战和未来方向.
结论:
- 人工智能,机器学习和生物标记分析的融合代表了对大动脉动脉瘤疾病的变革性前景.
- 持续的研发对于将这些技术转化为实际的诊断工具至关重要.
- 本综述为了解大动脉疾病管理当前进展和未来发展轨迹提供了一个框架.
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