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Early detection of visual impairment in young children using a smartphone-based deep learning system
Wenben Chen1, Ruiyang Li1, Qinji Yu2
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Insights
Early detection of visual impairment in young children is challenging. A new smartphone system, Apollo Infant Sight (AIS), analyzes gazing and facial features to identify over 16 ophthalmic disorders, improving screening accessibility.
Area of Science:
- Ophthalmology
- Pediatrics
- Mobile Health (mHealth)
Background:
- Early detection of visual impairment in young children is critical but hindered by limited cooperation with standard tests.
- Traditional screening methods struggle with real-world application, despite observable features in visually impaired children.
- Existing challenges necessitate innovative, accessible screening tools for early identification of pediatric visual disorders.
Purpose of the Study:
- To develop and validate a mobile health (mHealth) system for early detection of visual impairment in infants and young children.
- To assess the efficacy of the smartphone-based Apollo Infant Sight (AIS) system in identifying children with ophthalmic disorders.
- To evaluate the system's performance in both clinical settings and at-home use by untrained caregivers.
Main Methods:
- Development and validation of the Apollo Infant Sight (AIS) system using videos from 3,652 children (≤48 months).
- AIS records and analyzes gazing behaviors and facial features under visual stimuli to detect visual impairment.
- Prospective data collection for internal and external validation, including at-home implementation tests.
Main Results:
- AIS achieved an area under the receiver operating curve (AUC) of 0.940 in internal validation and 0.843 in external validation.
- The system demonstrated strong performance in at-home use by untrained parents/caregivers, achieving an AUC of 0.859.
- AIS successfully identified children with a wide range of 16 ophthalmic disorders, adapting to diverse testing conditions.
Conclusions:
- The smartphone-based Apollo Infant Sight (AIS) system offers a promising mHealth solution for early detection of visual impairment in young children.
- AIS has the potential to significantly improve screening accessibility and accuracy for pediatric ophthalmic disorders.
- The system empowers healthcare professionals, parents, and caregivers with a tool for identifying visual impairment across various settings.
Abstract:
Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging. Here, we present a mobile health (mHealth) system, the smartphone-based Apollo Infant Sight (AIS), which identifies visually impaired children with any of 16 ophthalmic disorders by recording and analyzing their gazing behaviors and facial features under visual stimuli. Videos from 3,652 children (≤48 months in age; 54.5% boys) were prospectively collected to develop and validate this system. For detecting visual impairment, AIS achieved an area under the receiver operating curve (AUC) of 0.940 in an internal validation set and an AUC of 0.843 in an external validation set collected in multiple ophthalmology clinics across China. In a further test of AIS for at-home implementation by untrained parents or caregivers using their smartphones, the system was able to adapt to different testing conditions and achieved an AUC of 0.859. This mHealth system has the potential to be used by healthcare professionals, parents and caregivers for identifying young children with visual impairment across a wide range of ophthalmic disorders.
