The Efficacy of an Artificial Intelligence Platform to Adapt Visual Aids for Patients With Low Vision: a Randomised Controlled Trial

Study Overview
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Brief Summary
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According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting. Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.

Official Title
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The Efficacy of an Artificial Intelligence Platform to Adapt Visual Aids for Patients With Low Vision: a Randomised Controlled Trial

Conditions
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Ophthalmology, Low Vision Aids, Artificial Intelligence

Intervention / Treatment
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* Other: Low vision aids


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Low vision Aged 3 to 105 Exclusion Criteria: Severe systemic diseases Failure to sign informed consent or unwilling to participate

Ages Eligible for Study
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Minimum Age: 3 Years
Maximum Age: 105 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Supportive Care
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Algorithm assisted group<br>Patients receive assisting devices fitting services from human doctors assisted by the machine learning model | Other: Low vision aids<br>* the assisting devices fitting for low-vision patients<br>|
| Experimental: Human doctor group<br>Patients receive assisting devices fitting services from humanr doctors | Other: Low vision aids<br>* the assisting devices fitting for low-vision patients<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The proportion of giving up assisting devices | The investigator will calculate the proportion of giving up more than one assisting devices in two groups for three months and six months | Baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time cost of using assisting devices of patients | The investigator will apply survival analysis for the time cost of using assisting devices in different groups. | Baseline |