Table 1 Data to be extracted from each study
Data category | Collected data |
|---|---|
Study characteristics | - Primary author |
- Publication year | |
- Recruitment period/study duration | |
- Country | |
- Study purpose | |
- Study type (e.g., RCT, prospective cohort study) | |
- Sample size | |
- Clinical setting (academic/community) | |
- Reference standard description (e.g., human graders—retina specialists) | |
- Ophthalmic condition screened for | |
- Funding sources | |
- Follow-up period | |
Patient information | - Patient sociodemographic data (including age (mean/median and categorization of pediatric and adult), sex, comorbidities, eye conditions, race/ethnicity, income status, education) |
- Inclusion and exclusion criteria | |
AI methods | - Imaging modalities used for screening (e.g., fundus photographs, ocular coherence tomography) |
- Automated algorithms or tools used (boosted tree, random forest, etc.) | |
- Role of AI in screening | |
- Number of human graders | |
- Number of ungradable images | |
- Identified pathologies (types and proportions) | |
Intervention outcomes | - Sensitivity/specificity |
- Positive predictive value | |
- Negative predictive value | |
- % correct as analyzed by artificial intelligence | |
- Diagnostic accuracy (if stated) |