Early detection of visual impairment in young children using a smartphone-based deep learning system.

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.

Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship.

BACKGROUND: Artificial intelligence (AI) is an increasingly popular tool in medical investigations. However, AI's potential of aiding medical teaching has not been explored. This study aimed to evaluate the effectiveness of AI-tutoring problem-based-learning (PBL) in ophthalmology clerkship and to assess the student evaluations of this module. METHODS: Thirty-eight Grade-two students in ophthalmology clerkship at Sun Yat-Sen University were randomly assigned to two groups. In Group A, students learned congenital cataracts through an AI-tutoring PBL module by exploring and operating an AI diagnosis platform. In Group B, students learned congenital cataracts through traditional lecture given with the same faculty. The improvement in student performance was evaluated by comparing the pre- and post-lecture scores of a specific designed test using paired-T tests. Student evaluations of AI-tutoring PBL were measured by a 17-item questionnaire. RESULTS: The post-lecture scores were significantly higher than the pre-lecture scores in both groups (Group A: P<0.0001, Group B: P<0.0001). The improvement of group A in the part of sign and diagnosis test (Part I) was more significant than that of group B (P=0.016). However, there was no difference in the improvement in the part of treatment plan test (Part II) between two groups (P=0.556). Overall, all respondents were satisfied and agreed that AI-tutoring PBL was helpful, effective, motive and beneficial to help develop critical and creative thinking. CONCLUSIONS: The application of AI-tutoring PBL into ophthalmology clerkship improved students' performance and satisfaction. AI-tutoring PBL teaching showed advantage in promoting students' understanding of signs of diseases. The instructors play an indispensable role in AI-tutoring PBL curriculum.

The Detrimental Effect of Noisy Visual Input on the Visual Development of Human Infants.

We followed visual development in a rare yet large sample of patients with congenital bilateral cataract for 4 years. We divided the patients into two groups: a complete deprivation group with no response to a flashlight pointing to either of their eyes and otherwise an incomplete deprivation group. All the patients received cataract surgery at age of 3 months. From 27 months onward, the complete deprivation group showed better developmental outcomes in acuity and eyeball growth than the incomplete deprivation group. Such a seemingly counterintuitive finding is consistent with research on visually deprived animals. Plasticity is better preserved in animals receiving a short period of complete visual deprivation from birth than in animals who saw diffuse light. The current finding that plasticity in visual development is better preserved in human infants with complete visual deprivation than in those who can see diffuse light but not patterned visual input has important clinical implications.

Dynamic profile of ocular refraction in pediatric cataract patients after lens surgeries.

AIM: To study the change in ocular refraction in patients with pediatric cataracts (PCs) after lens extraction. METHODS: A total of 1258 patients who were undergoing cataract extraction with/without intraocular lens (IOL) implantation were recruited during preoperative examinations between Jan 2010 and Oct 2013. Patient ages ranged from 1.5mo to 14y. Follow-ups were conducted at 1wk, 1, and 3mo postoperatively and every 3mo in the first year, then 6mo thereafter. Ocular refraction [evaluated as spherical equivalent (SE)] and yearly myopic shift (YMS) were recorded and statistically analyzed among patients with age at surgery, baseline ocular refraction, gender, postoperative time and laterality (bilateral vs unilateral). RESULTS: By Dec 31st 2015, 1172 participants had been followed for more than 2y. The median follow-up period was 3y. The critical factors affecting the ocular refraction of PC patients were baseline ocular refraction, postoperative time for both aphakic and pseudophakic eyes. YMS grew most rapidly in young childhood and early adolescence. CONCLUSION: After lens surgeries, ocular refraction in PC patients shows an individual difference of change. Further concerns should be raising to monitor the rapid myopic shift at early adolescence of these patients.

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who experienced varying degrees of visual loss but retained high behavioural plasticity, we show that behaviours with significantly higher occurrence in visually impaired infants can be identified, and that correlations between the frequency of specific behavioural patterns and visual-impairment severity, as well as variations in behavioural dynamics with age, can be quantified. We also show that a deep-learning algorithm (a temporal segment network) trained with the full-length videos can discriminate, for an independent dataset from 400 infants, mild visual impairment from healthy behaviour (area under the curve (AUC) of 85.2%), severe visual impairment from mild impairment (AUC of 81.9%), and various ophthalmological conditions from healthy vision (with AUCs ranging from 81.6% to 93.0%). The video dataset of behavioural phenotypes in response to visual loss and the trained machine-learning algorithm should help the study of visual function and behavioural plasticity in infants.