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

Not Applicable

• Conditions: Ophthalmology; Artificial Intelligence; Low Vision Aids
• Interventions: Other: Low vision aids

• Registration Number: NCT04919837
• 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: 2021-06
• Study First Submitted: 2021-06-06
• Study First Submitted QC: 2021-06-06
• Last Update Submitted: 2021-06-06
• Study First Posted: 2021-06-09
• Last Update Posted: 2021-06-09
• Primary Completion: 2021-07-27
• Study Completion: 2021-07-30

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• Low vision Aged 3 to 105

Exclusion Criteria

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Algorithm assisted group	Low vision aids	Patients receive assisting devices fitting services from human doctors assisted by the machine learning model
Human doctor group	Low vision aids	Patients receive assisting devices fitting services from humanr doctors

Primary Outcome Measures

Name	Time	Method
The proportion of giving up assisting devices	Baseline	The investigator will calculate the proportion of giving up more than one assisting devices in two groups for three months and six months

Secondary Outcome Measures

Name	Time	Method
Time cost of using assisting devices of patients	Baseline	The investigator will apply survival analysis for the time cost of using assisting devices in different groups.

Trial Locations

Locations (1)

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