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.
Study Type
OBSERVATIONAL
Enrollment
250
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.
Linkou Chang Gung Memorial Hospital
Taoyuan District, Taiwan
RECRUITINGAccuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment
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.
Time frame: Day 1 (single assessment at enrollment).
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