Video-based Assessment of Preschool Children's Gross Motor Development for Early Intervention Screening
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 250
- 试验地点
- 1
- 主要终点
- Accuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment
研究概览
简要总结
Artificial intelligence (AI) is currently one of the global focal points for industrial development, with its applications in healthcare steadily increasing, such as in disease prediction, image diagnosis, and drug development. AI assists healthcare professionals in clinical decision-making by training relevant models through algorithms, thereby enhancing medical efficiency and quality.
Currently, standardized tools are used in clinical settings to screen and assess various aspects of child development. Children's motor development levels are determined by comparing their performance against established norms. However, the current assessment methods primarily rely on on-site visual observation and recording by evaluators, which demands significant time and human resources.
This research aims to establish an automated screening tool for gross motor development in early intervention, suitable for independently walking children aged one to six years old in Taiwan. The goal is to reduce the time cost of manual assessment and enable remote healthcare applications.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 1 Year 至 6 Years（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Legal guardian willing to provide written informed consent.
- •Males and females aged 1 to 6 years old.
- •Capable of independent walking.
排除标准
- •Non-native Chinese speakers.
结局指标
主要结局
Accuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment
时间窗: Day 1 (single assessment at enrollment).
Accuracy will be calculated by comparing the AI model's classification results to pediatric therapists' assessments based on the CDIIT gross motor subscale. The accuracy formula is: (True Positive + True Negative) / Total number of cases.
次要结局
未报告次要终点