Objective:　Automatic　diabetic　retinopathy　screening　system　based　on　neural　networks　has　been　used　to　detect　diabetic　retinopathy　(DR).　However,　there　is　no　quantitative　synthesis　of　performance　of　these　methods.　We　aimed　to　estimate　the　sensitivity　and　specificity　of　neural　networks　in　DR　grading.　Methods:　Medline,　Embase,　IEEE　Xplore,　and　Cochrane　Library　were　searched　up　to　23　July　2019.　Studies　that　evaluated　performance　of　neural　networks　in　detection　of　moderate　or　worse　DR　or　diabetic　macular　edema　using　retinal　fundus　images　with　ophthalmologists?　judgment　as　reference　standard　were　included.　Two　reviewers　extracted　data　independently.　Risk　of　bias　of　eligible　studies　was　assessed　using　QUDAS-2　tool.　Results:　Twenty-four　studies　involving　235　235　subjects　were　included.　Quantitative　random-effects　meta-analysis　using　the　Rutter　and　Gatsonis　hierarchical　summary　receiver　operating　characteristics　(HSROC)　model　revealed　a　pooled　sensitivity　of　91.9%　(95%　CI:　89.6%　to　94.3%)　and　specificity　of　91.3%　(95%　CI:　89.0%　to　93.5%).　Subgroup　analyses　and　meta-regression　did　not　provide　any　statistically　significant　findings　for　the　heterogeneous　diagnostic　accuracy　in　studies　with　different　image　resolutions,　sample　sizes　of　training　sets,　architecture　of　convolutional　neural　networks,　or　diagnostic　criteria.　Conclusions:　State-of-the-art　neural　networks　could　effectively　detect　clinical　significant　DR.　To　further　improve　diagnostic　accuracy　of　neural　networks,　researchers　might　need　to　develop　new　algorithms　rather　than　simply　enlarge　sample　sizes　of　training　sets　or　optimize　image　quality.