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

NCT04919837 | Unknown DMC

• 1 other identifier: SFLVRCT-2020
• Study Type: interventional
• Target: 200
• Locations: 1 country
• Sites: 1

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.

Conditions

Ophthalmology; Low Vision Aids; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Time cost of using assisting devices of patients

  Baseline

Study Arms (2)

Algorithm assisted group

EXPERIMENTAL

Patients receive assisting devices fitting services from human doctors assisted by the machine learning model

Other: Low vision aids

Human doctor group

EXPERIMENTAL

Patients receive assisting devices fitting services from humanr doctors

Other: Low vision aids

Interventions

Low vision aids OTHER

the assisting devices fitting for low-vision patients

Algorithm assisted group; Human doctor group

Eligibility Criteria

• Age: 3 Years - 105 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Low vision Aged 3 to 105

You may not qualify if:

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

Sponsors & Collaborators

• Sun Yat-sen University: lead
• 2nd Affilliated Hospital of Fujian Medical University: collaborator

Study Sites (1)

2nd Affilliated Hospital of Jujian Medical University

Quanzhou, Fujian, 362000, China

RECRUITING

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 6, 2021
• First Posted: June 9, 2021
• Study Start: July 27, 2020
• Primary Completion: July 27, 2021
• Study Completion: July 30, 2021
• Last Updated: June 9, 2021

Record last verified: 2021-06

Locations

CN (1)