用算法自动分析婴儿视频，实现无控环境下新生儿运动评估。
Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings
- 从非受控环境视频中提取运动特征，适配多种设备与拍摄条件。
- 基于粗略标注数据，实现对婴儿运动质量的自动分类。
- 为新生儿神经发育筛查提供可扩展的自动化工具，适合临床推广。
一般运动（GMs）是婴儿自发的协调性身体运动，能反映神经系统发育情况。通过Prechtl GM评估（GMA）进行评估，GMs可有效预测神经发育障碍。然而，GMA需经过专门训练的临床医生，数量有限。为扩大新生儿筛查范围，亟需一种算法，能自动从婴儿视频中分类一般运动。这类数据面临录制时长、设备类型和环境差异等挑战，且每段视频仅粗略标注整体运动质量。本文提出一种工具，用于从这些视频中提取特征，并探索多种机器学习方法实现自动化GM分类。
原文摘要 · Abstract (English)
General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。