{"doi":"10.1167/tvst.15.6.32","title":"Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning","abstract":null,"journal":"Translational Vision Science & Technology","year":2026,"id":632026,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1638151,"name":"Xianggui Zhang","orcid":null,"position":1,"is_corresponding":false},{"id":1638152,"name":"Ruilong Li","orcid":null,"position":2,"is_corresponding":false},{"id":1599473,"name":"Qin Ding","orcid":null,"position":3,"is_corresponding":false},{"id":1638153,"name":"Ya Ye","orcid":null,"position":4,"is_corresponding":false},{"id":232357,"name":"Zhen Huang","orcid":"0000-0002-3990-7350","position":5,"is_corresponding":false},{"id":1439795,"name":"Cong Chen","orcid":"0000-0002-0270-383X","position":6,"is_corresponding":false},{"id":798367,"name":"Wenjing Zhang","orcid":"0000-0003-2615-8976","position":7,"is_corresponding":false},{"id":1638154,"name":"Lulu Tang","orcid":null,"position":8,"is_corresponding":false},{"id":1638155,"name":"Yanping Song","orcid":null,"position":9,"is_corresponding":false},{"id":255343,"name":"Ming Yan","orcid":"0000-0002-7126-5976","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning","abstract":"Purpose: The purpose of this study was to develop a deep learning algorithm capable of accurately classifying diabetic macular edema (DME) subtypes and segmenting the lesions in patients with diabetic retinopathy (DR) using structural optical coherence tomography (OCT) images. Methods: We retrospectively collected 3120 DME OCT B-scan images from 823 eyes of patients with DME, acquired from 4 different spectral-domain and swept-source OCT devices (Topcon 3D-2000, Topcon Triton, BK400K UWF SS-OCT, and VG200 SS-OCT) to enhance device diversity and evaluate cross-device generalizability. An annotation team, consisting of two mid-career ophthalmologists and one senior retinal specialist, performed meticulous multi-label annotations, including DME subtype categories, detection bounding boxes, and pixel-level segmentation masks, to build the DME-Seg dataset. Based on this dataset, we fine-tuned the YOLO11x-Seg model for the detection and segmentation tasks. Results: The fine-tuned model achieved promising performance on the DME-Seg dataset. For lesion detection, it attained an average mAP50(B) of 0.82, mAP50-95(B) of 0.56, and Dice coefficients of 0.82 ± 0.20 (95% confidence interval [CI] = 0.81-0.83). For segmentation, it achieved an mAP50(M) of 0.84, mAP50-95(M) of 0.54, and Dice coefficients of 0.79 ± 0.18 (95% CI = 0.78-0.80). Conclusions: The constructed DME-Seg dataset and the validated model demonstrate promising performance in the automated detection and segmentation of DME subtypes, with encouraging cross-device generalization capability. This resource provides a foundation for advancing artificial intelligence (AI)-assisted diagnosis and personalized treatment planning for DME. Translational Relevance: This automated quantification tool bridges the gap between AI research and clinical utility by assisting ophthalmologists in the precise diagnosis and treatment of DME.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42345636","pmcid":null,"openalex_id":"https://openalex.org/W7165938776","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.77630903,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.1167/tvst.15.6.32","host_type":"journal"},{"url":"https://doi.org/10.1167/tvst.15.6.32","host_type":"publisher"},{"url":"http://tvst.arvojournals.org/article.aspx?doi=10.1167/tvst.15.6.32","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/42345636","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13359094/","host_type":"repository"}],"fields_of_study":["Retinal Imaging and Analysis","Retinal Diseases and Treatments","Ocular Diseases and Behçet’s Syndrome"],"mesh_terms":["Deep Learning","Detection Algorithms","Algorithms","Diabetic Retinopathy","Humans","Macular Edema","Retrospective Studies","Tomography, Optical Coherence"],"keywords":["Optical coherence tomography","Segmentation","Deep learning","Diabetic retinopathy","Diabetic macular edema","Pattern recognition (psychology)","Image segmentation","Convolutional neural network"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T01:26:40.173278Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}