Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06540001
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-27
Brief Title:
Development and Validation of an Automated Self-administered Visual Acuity System
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development and Validation of an Automated Self-administered Visual Acuity System
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AutoVA
Brief Summary:
Visual acuity tests commonly conducted in clinics and used for health screenings are becoming more in demand due to an aging population Current online self-eye check apps are limited as they don39t accurately reflect true distance vision assessed in clinical settings These tests performed by trained personnel are time-consuming and can cause delays in clinics This project aims to develop an automated Visual Acuity VA station using AI technologies like speech-to-text and computer vision hypothesizing that it can match the accuracy of manual assessments by clinic staff thus potentially reducing waiting times and improving efficiency
Detailed Description:
Visual acuity is done as a routine eye check for the majority of eye patients in the clinic It is also done as a screening test for pre-employment health checks and health screening Patients can be checked for refractive errors on a community level or screened for eye diseases for those with chronic medical conditions With the increasing burden of aging population and eye conditions the number of patients in eye clinics will increase
There are a few existing online applications that allow self-eye checks however there are limitations They are usually done at an intermediate distance ie distance from phone to eye and does not accurately represent true distance vision Distance vision is typically set at 4- 6m in a clinical setting
A visual acuity test is administered by specially trained healthcare personnel such as optometrists and patient service assistants which is often time-consuming and labour intensive where one-on-one attention is required In addition vision is subjective and re-testing may be required at times to ensure accurate vision assessment
As the visual acuity test is the first clinical station patient goes to after registration this leads to a bottleneck in workflow causes delays in the subsequent services and eventually increases patient waiting times in the clinics
This project aims to develop and validate an automated Visual Acuity VA station through speech-to-text and computer vision technology in comparison to existing manual VA assessments
We hypothesize that we are able to use artificial intelligence to understand patient39s speech and posture to automate the visual acuity test We also hypothesize that the automated visual acuity test is comparable to having VA checked manually by a clinic staff
There are a few existing online applications that allow self-eye checks however there are limitations They are usually done at an intermediate distance ie distance from phone to eye and does not accurately represent true distance vision Distance vision is typically set at 4- 6m in a clinical setting
A visual acuity test is administered by specially trained healthcare personnel such as optometrists and patient service assistants which is often time-consuming and labour intensive where one-on-one attention is required In addition vision is subjective and re-testing may be required at times to ensure accurate vision assessment
As the visual acuity test is the first clinical station patient goes to after registration this leads to a bottleneck in workflow causes delays in the subsequent services and eventually increases patient waiting times in the clinics
This project aims to develop and validate an automated Visual Acuity VA station through speech-to-text and computer vision technology in comparison to existing manual VA assessments
We hypothesize that we are able to use artificial intelligence to understand patient39s speech and posture to automate the visual acuity test We also hypothesize that the automated visual acuity test is comparable to having VA checked manually by a clinic staff
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None