Purpose:　This　study　was　designed　to　apply　deep　learning　models　in　retinal　disease　screening　and　lesion　detection　based　on　optical　coherence　tomography　(OCT)　images.　Methods:　We　collected　37,138　OCT　images　from　775　patients　and　labelled　by　ophthal-mologists.　Multiple　deep　learning　models　including　ResNet50　and　YOLOv3　were　developed　to　identify　the　types　and　locations　of　diseases　or　lesions　based　on　the　images.　Results:　The　model　were　evaluated　using　patient-based　independent　holdout　set.　For　binary　classification　of　OCT　images　with　or　without　lesions,　the　performance　accuracy　was　98.5%,　sensitivity　was　98.7%,　specificity　was　98.4%,　and　the　F1　score　was　97.7%.　For　multiclass　multilabel　disease　classification,　the　models　was　able　to　detect　vitreomac-ular　traction　syndrome　and　age-related　macular　degeneration　both　with　an　accuracy　of　more　than　99%,　sensitivity　of　more　than　98%,　specificity　of　more　than　98%,　and　an　F1　score　of　more　than　97%.　For　lesion　location　detection,　the　recalls　for　different　lesion　types　ranged　from　87.0%　(epiretinal　membrane)　to　98.2%　(macular　pucker).　Conclusions:　Deep　learning-based　models　have　potentials　to　aid　retinal　disease　screen-ing,　classification　and　diagnosis　with　excellent　performance,　which　may　serve　as　useful　references　for　ophthalmologists.　Translational　Relevance:　The　deep　learning-based　models　are　capable　of　identify-ing　and　predicting　different　eye　diseases　and　lesions　from　OCT　images　and　may　have　potential　clinical　application　to　assist　the　ophthalmologists　for　fast　and　accuracy　retinal　disease　screening.　©　2023　The　Authors.