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The early visual screening of children plays a critical role in promoting visual development, especially for those with visual impairments. Among various approaches, eye-tracking based visual assessment has emerged as a promising tool, particularly for infants, toddlers, and children with developmental disabilities who are unable to complete traditional vision tests. The object of this study is to design and investigate the effectiveness of using a deep learning based, individualized eye-tracking system to assess visual function, specifically visual acuity and visual field, in typical preschool children and infants under the age of three. This study aims to establish a reliable, noninvasive visual screening method that accommodates the diverse needs and abilities of young children.
Full description
The investigators will recruit a total of 1,300 participants across four key groups to support the development and validation of an individualized visual function screening system using eye-tracking technology:
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Inclusion and exclusion criteria
Inclusion Criteria:
General adults:
Typically developing preschool children aged 3 to 5:
Children under 3 years old:
2. Exclusion Criteria
General adults:
Typically developing preschool children aged 3 to 5:
Children under 3 years old:
(1) Children with physical or mental disabilities (2) Children diagnosed with or suspected of having developmental delay (3) Children with obvious abnormalities in eye or facial appearance
________________________________________ B. Special Needs Group
B. Special Needs Group
Inclusion Criteria (1) Children under the age of 12 with special needs, including physical, mental, or multiple disabilities (2) The primary caregiver agrees to allow the child to undergo assessment and video recording using the "Deep Visual Tracking System"
Exclusion Criteria:
Exclusion Criteria
General adults:
Typically developing preschool children aged 3 to 5:
Children under 3 years old:
B. Special Needs Group 2. Exclusion Criteria
1,300 participants in 2 patient groups
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Central trial contact
Chiun-Ho Hou, PHD
Data sourced from clinicaltrials.gov
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