![]() Infancy and toddlerhood (early childhood) are critical periods for visual development 3, during which early detection and prompt treatment of ocular pathology can prevent irreversible visual loss 4, 5. ![]() Visual impairment is one of the most important causes of long-term disability in children worldwide and has a detrimental impact on education and socioeconomic achievements 1, 2. 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. 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. 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. Videos from 3,652 children (≤48 months in age 54.5% boys) were prospectively collected to develop and validate this system. 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. 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. Nature Medicine volume 29, pages 493–503 ( 2023) Cite this articleĮarly detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Early detection of visual impairment in young children using a smartphone-based deep learning system
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