Visual ImpairmentAutoVANCT06540001
Development and Validation of an Automated Self-administered Visual Acuity System
A study testing Automated visual acuity in people with Visual Impairment, run by Tan Tock Seng Hospital.
Trial 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 don\'t accurately reflect true distance vision assessed in clinical settings. Read more about this study
- Who is it for?
- Ages 21 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Visual Impairment
- What is being tested?
- Automated visual acuity
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2024to 1 Aug 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patients age \>21 and able to give consent
- Patients who have at least counting finger vision
- Patients who is able to speak in an audible and clear voice
- Patients who is able to use a digital device independently (e.g. handphone)
Who cannot join
- Patients on wheelchair/ walking aids
- Patients with hearing difficulties
- Patients with speech difficulties
- Patients who have cognitive impairment
- Patients who are hemiplegic/ motor dysfunction
- Patients who have vision worse than counting fingers
- Patients who are pregnant
Trial locations and contacts
No sites are listed yet.
About this study
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 don\'t 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.
Read the 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, i.e. 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 patient\'s 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.
- Conditions
- Visual Impairment
- Being tested
- Automated visual acuity
- Keywords
- Visual acuity
- Medical subject headings
- Vision Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Automated visual acuityDevice
The automated visual acuity device is developed in collaboration with Tan Tock Seng Hospital, Singapore Institute of Technology and Nanyang Technological University. It uses artificial intelligence for pose estimation and speech recognition to infer if the participant is reading the correct letters displayed on the screen.
| Study group | Role | What they receive |
|---|---|---|
| Patients will undergo both automated and manual visual acuity testing | Experimental | Patient will perform manual visual acuity first, then be guided to another room to have the visual acuity tested on the automated VA device Receives: Device: Automated visual acuity |
What is being measured
The primary outcome is the main question the study is designed to answer.
Best corrected visual acuity with and without pinhole using Snellen letters and numbers
Best corrected visual acuity will be expressed in metres (e.g. 6/6-1), and will be converted to LogMAR for analysis.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2024
- Study start1 Aug 2024 (estimated)
- Primary completion31 Oct 2024 (estimated)
- Study completion1 Aug 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Visual ImpairmentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tan Tock Seng Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Singapore Institute of Technology, Nanyang Technological University
- Responsible party
- Principal investigator, Li Zhenghao Kelvin, Tan Tock Seng Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06540001
- Sponsor's protocol ID
- 2024/00157
- Acronym
- AutoVA
- Status last verified
- Aug 2024
- First submitted
- 27 Jul 2024
- Last updated
- 6 Aug 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Automated Self-administered Visual Acuity System
- ClinicalTrials.gov ID
- NCT06540001
- Acronym / sponsor ID
- AutoVA / 2024/00157
- Conditions
- Visual Impairment
- Keywords
- Visual acuity
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Tan Tock Seng HospitalUniversity, hospital or other
- Collaborators
- Singapore Institute of Technology, Nanyang Technological University
- Study start
- 1 Aug 2024 (estimated)
- Primary completion
- 31 Oct 2024 (estimated)
- Study completion
- 1 Aug 2025 (estimated)
- First posted
- 6 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 don\'t 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, i.e. 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 patient\'s 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 design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients will undergo both automated and manual visual acuity testing | Experimental | Device: Automated visual acuityPatient will perform manual visual acuity first, then be guided to another room to have the visual acuity tested on the automated VA device |
Who may qualify
- Ages
- 21 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Patients age \>21 and able to give consent
- Patients who have at least counting finger vision
- Patients who is able to speak in an audible and clear voice
- Patients who is able to use a digital device independently (e.g. handphone)
Key exclusion criteria (7 listed)
- Patients on wheelchair/ walking aids
- Patients with hearing difficulties
- Patients with speech difficulties
- Patients who have cognitive impairment
- Patients who are hemiplegic/ motor dysfunction
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Automated visual acuityThe automated visual acuity device is developed in collaboration with Tan Tock Seng Hospital, Singapore Institute of Technology and Nanyang Technological University. It uses artificial intelligence for pose estimation and speech recognition to infer if the participant is reading the correct letters displayed on the screen. | Device | Patients will undergo both automated and manual visual acuity testing |
Primary outcome
Best corrected visual acuity with and without pinhole using Snellen letters and numbers
Time frame: 1 year
Best corrected visual acuity will be expressed in metres (e.g. 6/6-1), and will be converted to LogMAR for analysis.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
A study testing Automated visual acuity in people with Visual Impairment, run by Tan Tock Seng Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 6 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Kelvin Z Li., MBBS, MTech, FRCOphth (+6562566011, contact@ttsh.com.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Best corrected visual acuity with and without pinhole using Snellen letters and numbers (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2024.