心声图形的人工智能模型的开发和验证:叙述性审查
Sadie Bennett1,2, Casey L Johnson1, George Fisher3
1Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Journal of clinical medicine
|October 16, 2025
概括
人工智能 (AI) 可以提高心声回声学的一致性和效率. 本综述详细介绍了对心声回声学的AI开发和验证,解决挑战和未来方向.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 声心图对于心脏评估至关重要,但受到了操作员的变化.
- 人工智能 (AI) 集成提供了标准化心声回声学解释和提高效率的潜力.
- 在心声回声学中的AI应用范围从图像采集到预后分层.
研究的目的:
- 提供对人工智能模型开发和对心声回声学的验证的全面概述.
- 在心声回声学中探索AI实施的当前挑战.
- 概述人工智能在心声回声实践中的未来方向.
主要方法:
- 这篇叙事评论综合了现有的AI在心声回声学方面的文献.
- 它侧重于开发和验证AI模型的基本步骤.
- 审查审查了挑战和未来的前景.
主要成果:
- 人工智能模型在协助回声心脏图像采集,疾病检测和测量自动化方面表现有前途.
- 尽管人们越来越感兴趣,但对于心声回声学中的AI的严格开发和验证实践往往没有得到很好的描述.
- 为了安全有效地部署人工智能,需要采用标准化的方法.
结论:
- 人工智能有可能通过提高一致性和效率来彻底改变心声回声学.
- 人工智能模型开发和验证的明确准则对于临床采用至关重要.
- 未来的研究应该专注于解决当前的挑战,并探索新的AI应用在心声回声学.
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