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

June 6, 2021 updated by: Haotian Lin, Sun Yat-sen University

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Anticipated)

200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Fujian
      • Quanzhou, Fujian, China, 362000
        • Recruiting
        • 2nd Affilliated Hospital of Jujian Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

3 years to 105 years (Child, Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Low vision Aged 3 to 105

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

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

Arms and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The proportion of giving up assisting devices
Time Frame: Baseline
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
Time Frame: Baseline
The investigator will apply survival analysis for the time cost of using assisting devices in different groups.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sun Yat-sen University

Collaborators

2nd Affilliated Hospital of Fujian Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 27, 2020

Primary Completion (Anticipated)

July 27, 2021

Study Completion (Anticipated)

July 30, 2021

Study Registration Dates

First Submitted

June 6, 2021

First Submitted That Met QC Criteria

June 6, 2021

First Posted (Actual)

June 9, 2021

Study Record Updates

Last Update Posted (Actual)

June 9, 2021

Last Update Submitted That Met QC Criteria

June 6, 2021

Last Verified

June 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SFLVRCT-2020

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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