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Table 1 Data to be extracted from each study

From: Protocol for a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence for grading of ophthalmology imaging modalities

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)