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

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

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04892316
Enrollment
400
Registered
2021-05-19
Start date
2020-07-27
Completion date
2021-12-27
Last updated
2021-05-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Low Vision Aids, Ophthalmology

Keywords

Artificial Intelligence, Low Vision

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.

Interventions

DIAGNOSTIC_TESTDiagnostic test

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

Sponsors

Sun Yat-sen University
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
3 Years to 105 Years
Healthy volunteers
No

Inclusion criteria

* Low vision * Aged 3 to 105

Exclusion criteria

* Severe systemic disease * Failure to sign informed consent or unwilling to participate

Design outcomes

Primary

MeasureTime frameDescription
Accuracy of fitting results for assisting devicesbaselineThe investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth.

Secondary

MeasureTime frameDescription
Time cost for fitting assisting devicesbaselineThe investigator will calculate time cost for fitting assisting devices in different group.

Countries

China

Contacts

Primary ContactJianmin Hu, M.D., Ph.D.
doctorhjm@163.com+8615359595888

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026