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

NCT04892316 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2021-05-20

No results posted yet for this study

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
  • Artificial Intelligence
  • Low Vision Aids

Interventions

DIAGNOSTIC_TEST

Diagnostic test

The training dataset was used to train the model, which was validated and tested by the other two datasets.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Eligibility

Min Age
3 Years
Max Age
105 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-07-27
Primary Completion
2021-07-27
Completion
2021-12-27

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04892316 on ClinicalTrials.gov