aceDiagnostic Performance of Deep Learning Models for Ocular Toxoplasmosis Using Fundus Photography: A Systematic Review and Meta-Analysis
Background Ocular toxoplasmosis (OT) is the leading infectious cause of posterior uveitis worldwide, yet diagnosis remains challenging without a universal gold standard. Although deep learning (DL) shows significant promise for automated image analysis, a rigorous synthesis of its diagnostic accuracy across heterogeneous architectures and datasets is lacking. Methods Following PRISMA-DTA and CLAIM-AI guidelines, we searched major databases through January 2026 for studies evaluating DL models for OT detection us…
Across nine meta-analyzed studies, deep learning models detected ocular toxoplasmosis on fundus photographs with high pooled sensitivity and specificity.
Most included studies used one Paraguayan dataset and lacked multicentric external validation, which limits how far the accuracy can be generalized to other populations.
Most included studies used a single Paraguayan dataset, with high heterogeneity and no multicentric external validation, so global generalizability is uncertain.
Evidence
- Peer-reviewedOcular Immunology and Inflammation2026-10-07
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Truvace Impact Record TRV-2026-1770, v1: “Diagnostic Performance of Deep Learning Models for Ocular Toxoplasmosis Using Fundus Photography: A Systematic Review and Meta-Analysis.” Truvace, 2026-10-18. /record/TRV-2026-1770 (accessed at citation time). sha256 4d6c050f7d84281f…
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