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

May 19, 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

Observational

Enrollment (Anticipated)

400

Contacts and Locations

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

Study Contact

  • Name: Jianmin Hu, M.D., Ph.D.
  • Phone Number: +8615359595888
  • Email: doctorhjm@163.com

Study Locations

  • China
    • Fujian
      • Quanzhou, Fujian, China, 362000
        • Recruiting
        • 2nd Affilliated Hospital of Fujian 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

Sampling Method

Non-Probability Sample

Study Population

Visually disabled patients were referred by the Town Disability Federation in Fujian Province and Guangdong Province

Description

Inclusion Criteria:

  • Low vision
  • Aged 3 to 105

Exclusion Criteria:

  • Severe systemic disease
  • 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

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of fitting results for assisting devices
Time Frame: baseline
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
Time Frame: baseline
The investigator will calculate time cost for fitting assisting devices in different group.
baseline

Collaborators and Investigators

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

Sponsor

Sun Yat-sen 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)

December 27, 2021

Study Registration Dates

First Submitted

May 17, 2021

First Submitted That Met QC Criteria

May 18, 2021

First Posted (Actual)

May 19, 2021

Study Record Updates

Last Update Posted (Actual)

May 20, 2021

Last Update Submitted That Met QC Criteria

May 19, 2021

Last Verified

May 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SFLV-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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