Using Machine Learning to Adapt Visual Aids for Patients With Low Vision

Study Overview
=================
Brief Summary
-----------------
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
-----------------
Using Machine Learning to Adapt Visual Aids for Patients With Low Vision

Conditions
-----------------
Ophthalmology, Artificial Intelligence, Low Vision Aids

Intervention / Treatment
-----------------
* Diagnostic Test: Diagnostic test


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

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

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Junior doctor group<br>Patients receive assisting devices fitting services from junior doctors | Diagnostic Test: Diagnostic test<br>* The training dataset was used to train the model, which was validated and tested by the other two datasets.<br>|
| Senior doctor group<br>Patients receive assisting devices fitting services from senior doctors | Diagnostic Test: Diagnostic test<br>* The training dataset was used to train the model, which was validated and tested by the other two datasets.<br>|
| Algorithm assisted group<br>Patients receive assisting devices fitting services from junior doctors assisted by the machine learning model | Diagnostic Test: Diagnostic test<br>* The training dataset was used to train the model, which was validated and tested by the other two datasets.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of fitting results for assisting devices | The investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth. | baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time cost for fitting assisting devices | The investigator will calculate time cost for fitting assisting devices in different group. | baseline | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Artificial Intelligence, Low Vision