Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
1 other identifier
observational
400
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2020
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 27, 2020
CompletedFirst Submitted
Initial submission to the registry
May 17, 2021
CompletedFirst Posted
Study publicly available on registry
May 19, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 27, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
December 27, 2021
CompletedMay 20, 2021
May 1, 2021
1 year
May 17, 2021
May 19, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Accuracy of fitting results for assisting devices
The investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth.
baseline
Secondary Outcomes (1)
Time cost for fitting assisting devices
baseline
Study Arms (3)
Junior doctor group
Patients receive assisting devices fitting services from junior doctors
Senior doctor group
Patients receive assisting devices fitting services from senior doctors
Algorithm assisted group
Patients receive assisting devices fitting services from junior doctors assisted by the machine learning model
Interventions
The training dataset was used to train the model, which was validated and tested by the other two datasets.
Eligibility Criteria
Visually disabled patients were referred by the Town Disability Federation in Fujian Province and Guangdong Province
You may qualify if:
- Low vision
- Aged 3 to 105
You may not qualify if:
- Severe systemic disease
- Failure to sign informed consent or unwilling to participate
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
2nd Affilliated Hospital of Fujian Medical University
Quanzhou, Fujian, 362000, China
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator
Study Record Dates
First Submitted
May 17, 2021
First Posted
May 19, 2021
Study Start
July 27, 2020
Primary Completion
July 27, 2021
Study Completion
December 27, 2021
Last Updated
May 20, 2021
Record last verified: 2021-05