Research on Intelligent Adaptation of Assistive Devices for Patients with Low Vision Based on Deep Learning
Objective:To establish a neural network model of intelligent assistive test for patients with low vision in rural areas with mobile home visit.Methods:A total of 728 patients from the Rehabilitation Guidance Center for the visually Impaired in the Second Affiliated Hospital of Fujian Medical University from May 2019 to May 2023 were selected as the research objects.Combined with descriptive research methods,an intelligent assistive device adaptation model based on neural network algorithm was constructed and verified.Results:Regarding the use of low vision aids,71.15%of patients had no more than two aids,while 28.85%of patients had three or more aids.In the degree of visual impairment,mild visual impairment accounted for 10.44%,moderate visual impairment accounted for 43.27%,severe visual impairment accounted for 26.51%,and blindness accounted for 19.78%.After comprehensive analysis,the accuracy was selected as the main evaluation standard of the model performance,and the F1 value was used as the auxiliary evaluation standard.When the model threshold was 0.4,the accuracy was about 80%,and the F1 value was about 0.31,which could be used as the threshold for classification and judgment of the model.Conclusion:The adaptation of assistive devices in patients with low vision in rural areas of China is closely related to visual function,quality of life and rehabilitation needs.It is of certain clinical application value to construct an intelligent adaptive neural network model of assistive devices for mobile out-patient mode of low vision patients in rural areas.