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基于超声波成像识别产前异常:一个系统的临床工程审查
Natarajan Sriraam1, Babu Chinta1, Seshadhri Suresh2
1Center for Medical Electronics and Computing, Dept of Medical Electronics, Ramaiah Institute of Technology (RIT), Bangalore, India.
Progress in biomedical engineering (Bristol, England)
|December 10, 2024
概括
人工智能 (AI) 通过改进超声波 (US) 图像分析来提高产前查,以早期检测异常. 可解释性AI (XAI) 对于建立信任和将这些先进的诊断工具整合到临床实践中至关重要.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 在产前医学和产科.
背景情况:
- 超声波 (US) 影像对于产前查至关重要,可以实时评估胎儿解剖学.
- 目前的诊断可靠性受到操作员专业知识和设备限制的限制.
- 第一个三个月的查面临技术挑战,影响高风险怀孕的识别.
研究的目的:
- 探索工程进步,以提高产前查的效率,准确性和一致性.
- 研究人工智能 (AI) 和可解释AI (XAI) 在自动诊断中的作用.
- 促进数据驱动的产前诊断解决方案的信任和临床整合.
主要方法:
- 开发基于人工智能的美国图像处理和多式联通数据融合技术.
- 实现可解释的人工智能 (XAI),以实现视觉解释和决策透明度.
- 一个深度学习框架,用于从美国视频源生成图表,用于异常选.
主要成果:
- 应用于生成图表的AI建模可以将胎儿缺陷与可能的变形相关联.
- 工程方法提高了产前诊断中的成像质量,自动化和可解释性.
- 拟议的方法旨在提高异常检测的整体准确性和一致性.
结论:
- 先进的工程,包括人工智能和XAI,对改变产前护理工作流程具有重大前景.
- 增加自动化和可解释性可以扩大诊断能力,以更好地检测胎儿异常.
- 通过透明度建立信任对于AI驱动的产前查工具的临床采用至关重要.
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