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

• Conditions: Artificial Intelligence; Low Vision Aids; Ophthalmology
• Interventions: Diagnostic Test: Diagnostic test

• Registration Number: NCT04892316
• Lead Sponsor: Sun Yat-sen University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2020-07-27
• Status Verified: 2021-05
• Study First Submitted: 2021-05-17
• Study First Submitted QC: 2021-05-18
• Study First Posted: 2021-05-19
• Last Update Submitted: 2021-05-19
• Last Update Posted: 2021-05-20
• Primary Completion: 2021-07-27
• Study Completion: 2021-12-27

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

• Low vision
• Aged 3 to 105

Exclusion Criteria

• Severe systemic disease
• Failure to sign informed consent or unwilling to participate

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Junior doctor group	Diagnostic test	Patients receive assisting devices fitting services from junior doctors
Algorithm assisted group	Diagnostic test	Patients receive assisting devices fitting services from junior doctors assisted by the machine learning model
Senior doctor group	Diagnostic test	Patients receive assisting devices fitting services from senior doctors

Primary Outcome Measures

Name	Time	Method
Accuracy of fitting results for assisting devices	baseline	The investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth.

Secondary Outcome Measures

Name	Time	Method
Time cost for fitting assisting devices	baseline	The investigator will calculate time cost for fitting assisting devices in different group.

Trial Locations

Locations (1)

2nd Affilliated Hospital of Fujian Medical University; 🇨🇳 Quanzhou, Fujian, China