Visual Impairment
Conditions
Keywords
Visual impairment, Community Screening, Randomized Controlled Trial (RCT), Artificial Intelligence (AI)
Brief summary
The goal of this observational study is to evaluate the performance, operational efficiency, acceptability, feasibility, and cost-effectiveness of an AI-assisted screening model for visual impairment in a community setting. The main questions it aims to answer are: * Can the AI-assisted screening model improve screening and referral accuracy compared to the current traditional screening approach? * Does the AI-assisted model enhance operational efficiency and reduce healthcare costs in a community setting? Researchers will compare the AI-assisted model with the current traditional screening approach to assess its impact on screening accuracy, operational efficiency, and cost-effectiveness. Participants will: * Undergo vision screening using either the AI-assisted model or the traditional model. * Provide feedback on the acceptability of the screening approach. * Contribute to evaluating the feasibility and costs associated with each screening method.
Interventions
DEVICEAI
Retinal photography-based deep learning algorithm for detection of disease-related visual impairment cases
Sponsors
Singapore Eye Research Institute
Institute of High Performance Computing (IHPC), A*STAR Research Institutes
National University Polyclinics, Singapore
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Individuals aged 50 years old and above.
Exclusion criteria
* Individuals aged below 50 years old.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of AVIRI on the detection of visual impairment | through study completion, an average 1 year | The primary outcome is the detection performance for VI (refractive error-related and disease-related VI) and the rate of correct referral, with reference to the expert panels' diagnosis . To assess whether the new AI-assisted model has better referral accuracy than the current traditional model, the accuracy, AUC values, sensitivity, specificity and other performance metrics of the two models will be calculated and compared. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Operational efficiency | through study completion, an average 1 year | Evaluation includes (i) average screening time per patient and (ii) average number of patients screened per session. |
| Patient acceptability | through study completion, an average 1 year | Patient Acceptability is assessed via a 6-item questionnaire (4-point Likert scale: 'Satisfaction' or 'Likelihood') administered by trained coordinators. |
| Perceptions of feasibility | through study completion, an average 1 year | Perceptions of feasibility is evaluated through at least two focus groups with optometrists and one with service providers will be conducted, guided by the Consolidated Framework for Implementation Research (CFIR). Discussions will explore barriers and facilitators influencing the AI screening model's adoption. Data will be inductively and deductively coded by two researchers using CFIR constructs; discrepancies resolved through consensus or a third researcher. |
| Cost savings of implementing the AI-assisted screening model | through study completion, an average 1 year | Researchers will quantify the incremental cost savings of implementing the AI-assisted screening model over the PSS model using an Activity Based Costing (ABC) approach that quantifies all non-sunk costs (including labor, materials and supplies, and amortized technology and space utility/ rental costs) required to conduct each assessment stratified by key activities of each screening model (e.g., conduct screening examinations, operationalize the AVIRI algorithm, on-site generation of test results etc.). Fixed costs will be amortized over the inputs' expected useful life (i.e. involved fixed assets' life expectancy). For the cost of clinical assessments, researchers will use non-subsidized bill sizes as these are expected to approximate actual costs. |
Countries
Singapore
Outcome results
None listed