Objectives To analyse and compare the efficacy of different interventions for myopia prevention and control in children. Methods We searched CNKI, VIP, Wan-Fang, CBM, Chinese Clinical Registry, PubMed, The Cochrane Library, Web of Science, Embase and ClinicalTrials.gov from inception to July 2022. We selected randomized controlled trials (RCTs) that included interventions to slow myopia progression in children. The main outcomes included mean annual change in axial length (AL) (millimetres/year) and in refraction (R) (dioptres/year). Results A total of 80 RCTs (27103 eyes) were included. In comparison with control, orthokeratology (AL, −0.36 [−0.53, −0.20], P < 0.05; R, 0.56 [0.34, 0.77], P < 0.05), 1%Atropine (AL, −0.39 [−0.65, −0.13], P < 0.05; R, 0.54 [0.31, 0.77], P < 0.05), 0.01%Atropine + orthokeratology (AL, −0.47 [−0.80, −0.14], P < 0.05; R, 0.81 [0.43, 1.20], P < 0.05) could significantly slow the progression of myopia; in addition, progressive multi-focal spectacle lenses (PMSL) (0.42, [0.06, 0.79], P < 0.05), bifocal soft contact lenses (0.40, [0.03, 0.77], P < 0.05), 0.5%Atropine (0.67 [0.25, 1.10], P < 0.05), 0.1%Atropine (0.42 [0.15, 0.71], P < 0.05), 0.05%Atropine (0.57 [0.28, 0.86], P < 0.05), 0.01%Atropine (0.33 [0.15, 0.52], P < 0.05), 1%Atropine + bifocal spectacle lenses (BSL) (1.30 [0.54, 2.00], P < 0.05), 1%Atropine + PMSL (0.66 [0.23, 1.10], P < 0.05), 0.01%Atropine + single vision spectacle lenses (SVSL) (0.70 [0.23, 1.10], P < 0.05), 0.01%Atropine + orthokeratology (0.81 [0.43, 1.20], P < 0.05), BSL + Massage (0.85 [0.22, 1.50], P < 0.05), SVSL + Red light (0.59 [0.06, 0.79], P < 0.05) showed significant slowing effect on the increase in R. Conclusions This network meta-analysis suggests that the combined measures were most effective in AL and R, followed by Atropine.

Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.