开发一种深度学习方法,用于使用超声波图像在新生儿身上自动预测身体成分
Keshi He1, Y I Li2, Hayoung Cho1
1Department of Engineering, Boston College, Chestnut Hill, MA 02446, USA.
IEEE access : practical innovations, open solutions
|January 26, 2026
概括
这项研究开发了一种深度学习方法,使用超声波图像自动预测婴儿的身体组成,包括脂肪质量和无脂肪质量. 这项新方案对评估早产婴儿营养不良有前途.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确测量人体成分,包括脂肪质量 (FM) 和无脂肪质量 (FFM),对于评估营养不良和营养干预措施的有效性至关重要.
- 目前用于身体成分分析的方法可能是侵入性的或不可访问的,特别是对于像早产婴儿这样的弱势群体.
研究的目的:
- 开发和验证一个新的超声波扫描协议,与深度学习管道集成,用于自动预测身体成分.
- 确定最佳的数据处理技术,合适的深度学习模型,以及用于预测FM和FFM的关键解剖位置.
主要方法:
- 对一组临床数据集的分析,其中包括早产婴儿 (n=65) 的双肩,腹部和四头四腿的超声图像.
- 采用空气位移膜学 (ADP) 进行地面真实FM和FFM测量.
- 采用预处理技术 (无声化,中位数过,数据增强) 和修改的EfficientNet-B1架构用于自动预测.
主要成果:
- 预处理方法显著提高了预测性能.
- 经过修改的EfficientNet-B1架构使得从超声波图像中完全自动预测身体组成.
- 使用双肩和四头骨 (26.1% MAPE) 或双肩,四头骨和腹部 (25.32% MAPE) 扫描位置,可以实现最佳的预测准确度.
结论:
- 这项研究首次展示了深度学习用于使用超声波图像进行自动化身体组成预测的演示.
- 开发的协议为评估婴儿营养不良的新型非侵入性方法提供了基础.
- 灵敏度分析表明,不同的身体部位和组织厚度会影响FM和FFM的预测.
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