Gross Motor Development Delay
Conditions
Keywords
artificial intelligence, machine learning, neural networks
Brief summary
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.
Interventions
OTHERGross Motor Development Screening Tool
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.
Sponsors
National Taiwan Normal University
Chang Gung Memorial Hospital
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
1 Years to 6 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment | 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. |
Countries
Taiwan
Contacts
Primary Contactyijing li
Outcome results
None listed