Improving myopia awareness via school-based myopia prevention health education among Chinese students.

AIM: To investigate the myopia awareness level, knowledge, attitude, and skills at baseline and to implement and evaluate the efficacy of myopia prevention health education among Chinese students. METHODS: A total of 1000 middle school students from 2 middle schools were invited to participate in the study, and myopia prevention health education was conducted. The students were assessed at baseline, followed by a survey. The efficacy of health education was evaluated using the self-comparison method pre- and post-health education. RESULTS: The study included 957 and 850 pre- and post-health education participants, respectively. The baseline knowledge of all respondents on myopic symptoms (87.5%), myopia is a risk of eyes (72.9%), myopia prevention (91.3%), myopia increases with age (86.7%), performing periodic eye examinations (92.8%), and one first, one foot, and one inch (84.8%) significantly increased after health education (P<0.001 for all). However, the percentage of students who still did not think it necessary to take breaks after 30-40min of continuous near work was 27.0%. The opinion that "myopia can be cured" was still present in 38.3%. CONCLUSION: Implementing school-based myopia prevention health education improves knowledge, attitudes, and skills regarding myopia among Chinese middle school students.

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.

[Study on preferred retinal locus].

Preferred retinal locus (PRL) is always found in the age-related macular degeneration and other macular damages in patients with low vision, and it is a very important anatomic position in patients with central vision impairment to achieve the rehabilitation. In recent years, the training of preferred retinal locus (PRL) has become a research hotspot of low vision rehabilitation, it can clearly improve functional vision and quality of life. The authors reviewed relevant literatures, and summarized the definition, position, characteristics, training and clinical implications of the PRL.

Application of neural network model in assisting device fitting for low vision patients.

BACKGROUND: To explore the application of neural network models in artificial intelligence (AI)-aided devices fitting for low vision patients. METHODS: The data of 836 visually impaired people were collected in southwestern Fujian from May 2014 to May 2017. After a full eye examination, 629 low vision patients were selected from this group. Based on the visual functions, rehabilitation needs, and living quality scores of the selected patients, the professionals chose assistive devices that were the best fit for the patients. The data of these three factors were then subjected to the quantitative analysis, and the results were digitized and labeled. The final datasets were used to train a fully connected deep neural networks to obtain an AI-aided model for assistive device fitting. RESULTS: In this study, the main causes of low vision in southwestern Fujian were congenital diseases, among which congenital cataract was the most common. During the low vision AI-aided devices fitting, we found that the intermediate distance magnifier was suitable for the largest number of patients. Through quantitative analysis of the research results, it was found that AI-aided devices fitting was closely related to visual function, rehabilitation needs and quality of life. If this complex relationship can be mapped into the neural network model, AI-aided device fitting can be realized. We built a fully connected neural network model for AI-aided device fitting. The input of the model was the characteristic data of low vision patients, and the output was the forecast of suitable devices. When the threshold of the model was 0.4, the accuracy was about 80% and the F1 value was about 0.31. This threshold can be used as the classification judgment threshold of the model. CONCLUSIONS: Low vision AI-aided device fitting is closely related to visual function, rehabilitation needs, and quality of life scores. The neural network model based on full connection can achieve high accuracy in AI-aided devices fitting. It has a great impact on clinical application.