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NCT Number: NCT07054437

Video-based Assessment of Preschool Children's Gross Motor Development

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.

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Key information

Conditions

Gross Motor Development Delay

Age range

1 year–6 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Legal guardian willing to provide written informed consent.
  • Males and females aged 1 to 6 years old.
  • Capable of independent walking.

Exclusion criteria

  • Non-native Chinese speakers.

Treatment and study plan

Gross Motor Development Screening Tool

Other

This intervention is an automated gross motor development screening tool specifically designed for independently walking children aged one to six years old in Taiwan. What sets it apart is its use of artificial intelligence (AI) algorithms to analyze motion data, enabling early identification of potential gross motor developmental delays.

Unlike traditional methods that rely on manual, visual observation and subjective recording by healthcare professionals, this tool aims to significantly reduce assessment time and human resource costs. Furthermore, its automated nature makes it uniquely suited for telemedicine applications, allowing for remote screenings and overcoming geographical barriers to access early intervention services. The tool will be developed and validated against established developmental norms relevant to the Taiwanese population.

Primary outcomes

  1. Accuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

yijing li

CONTACT

[email protected]

+886-920226108

Sponsors and collaborators

Lead sponsor

Chang Gung Memorial Hospital

Other

Collaborators

  • National Taiwan Normal University

Registry information

Official study title

Video-based Assessment of Preschool Children's Gross Motor Development for Early Intervention Screening

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jul 8, 2025
Registry last updated
Jul 8, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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