ObjectiveTo systematically review the efficacy of defocus incorporated multiple segments (DIMS) spectacle lenses and orthokeratology (Ortho-K) in controlling myopia. MethodsThe PubMed, Embase, Cochrane Library, Web of Science, CBM, WanFang Data and CNKI databases were electronically searched to collect clinical studies related to the objectives from January 2000 to June 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.3 software. ResultsA total of 8 RCTs and 7 cohort studies were included. The results of meta-analysis showed that both Ortho-K lens and DIMS had better axial control effects than the single vision control group (MD=?0.18, 95%CI ?0.21 to ?0.15, P<0.01; MD=?0.21, 95%CI ?0.27 to ?0.15, P<0.01). The Ortho-K had a smaller one-year growth in axial length compared to the DIMS (MD=?0.06, 95%CI ?0.08 to ?0.04, P<0.01). ConclusionCurrent evidence suggests that Ortho-K and DIMS have better myopia control effects than single lens lenses, while Ortho-K has better myopia control effects than DIMS, but the advantages are not significant. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo evaluate the diagnostic performance of artificial intelligence (AI) in strabismus detection through a systematic review and meta-analysis. MethodsThe PubMed, Embase, Cochrane Library, Web of Science, CBM, WanFang Data, and CNKI databases were systematically searched to identify clinical studies on AI-assisted strabismus diagnosis. Study quality was assessed using the QUADAS-AI tool. Diagnostic performance metrics were pooled using a random-effects model. Meta-regression and subgroup analyses were performed to explore sources of heterogeneity. ResultsNine studies involving 19872 participants were included. Meta-analysis revealed a pooled sensitivity of 0.94 (95%CI 0.88 to 0.97) and a pooled specificity of 0.95 (95%CI 0.90 to 0.98) in internal validation test sets. For independent external validation cohorts, the pooled sensitivity reached 0.97 (95%CI 0.93 to 1.00) and specificity was 0.99 (95%CI 0.98 to 1.00). Four studies (44%) had a high risk of bias. Meta-regression and subgroup analyses indicated that the performance of AI in diagnosing strabismus was associated with sample size, validation method, use of transfer learning, and feature extraction type. ConclusionAI demonstrates excellent sensitivity and specificity for strabismus screening and early diagnosis, showing potential as a clinical decision support tool. Future studies require standardized methodologies and rigorous external validation to ensure clinical applicability.