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

Sun Yat-sen University

Status: Unknown

Conditions: Artificial Intelligence; Ophthalmology; Low Vision Aids

Treatments: Diagnostic Test: Diagnostic test

Study type: Observational

Funder types: Other

Identifiers

NCT04892316

SFLV-2020

Details and patient eligibility

About

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.

Enrollment: 400 estimated patients

Sex: All

Ages: 3 to 105 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Low vision
• Aged 3 to 105

Exclusion criteria

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

Trial design

400 participants in 3 patient groups

Junior doctor group

Description:

Patients receive assisting devices fitting services from junior doctors

Treatment:

Diagnostic Test: Diagnostic test

Senior doctor group

Description:

Patients receive assisting devices fitting services from senior doctors

Treatment:

Diagnostic Test: Diagnostic test

Algorithm assisted group

Description:

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

Treatment:

Diagnostic Test: Diagnostic test