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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)