{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,15]],"date-time":"2026-11-15T08:36:29Z","timestamp":1794731789655,"version":"4.9.1"},"reference-count":98,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioMed Eng OnLine"],"DOI":"10.1186\/s12938-025-01336-1","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T18:55:41Z","timestamp":1741978541000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3302-440X","authenticated-orcid":false,"given":"Mehrsa","family":"Moannaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8856-4350","authenticated-orcid":false,"given":"Faezeh","family":"Jadidian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahereh","family":"Doustmohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir Mohammad","family":"Kiapasha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romina","family":"Bayani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8703-2966","authenticated-orcid":false,"given":"Mohammadreza","family":"Rahmani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Jahanbazy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fereshteh","family":"Sohrabivafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahsa","family":"Asadi Anar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amin","family":"Magsudy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyed Kiarash","family":"Sadat Rafiei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaser","family":"khakpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"1336_CR1","doi-asserted-by":"publisher","first-page":"1077669","DOI":"10.3389\/fendo.2022.1077669","volume":"13","author":"T-E Tan","year":"2023","unstructured":"Tan T-E, Wong TY. Diabetic retinopathy: looking forward to 2030. Front Endocrinol. 2023;13:1077669.","journal-title":"Front Endocrinol"},{"issue":"1","key":"1336_CR2","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.survophthal.2021.04.003","volume":"67","author":"M Nebbioso","year":"2022","unstructured":"Nebbioso M, et al. Diabetic retinopathy, oxidative stress, and sirtuins: an in depth look in enzymatic patterns and new therapeutic horizons. Surv Ophthalmol. 2022;67(1):168\u201383.","journal-title":"Surv Ophthalmol"},{"issue":"11","key":"1336_CR3","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","volume":"128","author":"ZL Teo","year":"2021","unstructured":"Teo ZL, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580\u201391.","journal-title":"Ophthalmology"},{"issue":"Supplement_1","key":"1336_CR4","doi-asserted-by":"publisher","first-page":"S105","DOI":"10.2337\/dc18-S010","volume":"41","author":"AD Association","year":"2018","unstructured":"Association AD. 10. Microvascular complications and foot care: standards of medical care in diabetes\u20142018. Diabetes Care. 2018;41(Supplement_1):S105\u201318.","journal-title":"Diabetes Care"},{"issue":"7","key":"1336_CR5","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1038\/ejcn.2017.40","volume":"71","author":"R Pradeepa","year":"2017","unstructured":"Pradeepa R, Mohan V. Prevalence of type 2 diabetes and its complications in India and economic costs to the nation. Eur J Clin Nutr. 2017;71(7):816\u201324.","journal-title":"Eur J Clin Nutr"},{"key":"1336_CR6","doi-asserted-by":"publisher","first-page":"16012","DOI":"10.1038\/nrdp.2016.12","volume":"2","author":"TY Wong","year":"2016","unstructured":"Wong TY, et al. Diabetic retinopathy. Nat Rev Dis Primers. 2016;2:16012.","journal-title":"Nat Rev Dis Primers"},{"key":"1336_CR7","volume-title":"Diabetic retinopathy","author":"UV Shukla","year":"2024","unstructured":"Shukla UV, Tripathy K. Diabetic retinopathy. Treasure Island: StatPearls Publishing; 2024."},{"issue":"4","key":"1336_CR8","doi-asserted-by":"publisher","first-page":"247","DOI":"10.4103\/0970-0218.91324","volume":"36","author":"P Vashist","year":"2011","unstructured":"Vashist P, et al. Role of early screening for diabetic retinopathy in patients with diabetes mellitus: an overview. Indian J Community Med. 2011;36(4):247.","journal-title":"Indian J Community Med"},{"issue":"2","key":"1336_CR9","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/nrendo.2017.151","volume":"14","author":"Y Zheng","year":"2018","unstructured":"Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88\u201398.","journal-title":"Nat Rev Endocrinol"},{"key":"1336_CR10","doi-asserted-by":"publisher","first-page":"1197783","DOI":"10.3389\/fendo.2023.1197783","volume":"14","author":"Z Wang","year":"2023","unstructured":"Wang Z, et al. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol. 2023;14:1197783.","journal-title":"Front Endocrinol"},{"issue":"46","key":"1336_CR11","first-page":"21","volume":"16","author":"K Viswanath","year":"2003","unstructured":"Viswanath K, McGavin DM. Diabetic retinopathy: clinical findings and management. Community Eye Health. 2003;16(46):21.","journal-title":"Community Eye Health"},{"issue":"1","key":"1336_CR12","volume":"1","author":"G Virgili","year":"2015","unstructured":"Virgili G, et al. Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst Rev. 2015;1(1): CD008081.","journal-title":"Cochrane Database Syst Rev"},{"issue":"4","key":"1336_CR13","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.optom.2014.07.002","volume":"8","author":"R Heitmar","year":"2015","unstructured":"Heitmar R, et al. Comparison of subjective and objective methods to determine the retinal arterio-venous ratio using fundus photography. J Optom. 2015;8(4):252\u20137.","journal-title":"J Optom"},{"issue":"22","key":"1336_CR14","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1001\/jama.2016.17563","volume":"316","author":"TY Wong","year":"2016","unstructured":"Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA. 2016;316(22):2366\u20137.","journal-title":"JAMA"},{"issue":"8","key":"1336_CR15","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s12032-022-01711-1","volume":"39","author":"S Quazi","year":"2022","unstructured":"Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022;39(8):120.","journal-title":"Med Oncol"},{"issue":"2","key":"1336_CR16","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","volume":"103","author":"DSW Ting","year":"2019","unstructured":"Ting DSW, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167\u201375.","journal-title":"Br J Ophthalmol"},{"issue":"9","key":"1336_CR17","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1007\/s00125-022-05727-0","volume":"65","author":"J Grauslund","year":"2022","unstructured":"Grauslund J. Diabetic retinopathy screening in the emerging era of artificial intelligence. Diabetologia. 2022;65(9):1415\u201323.","journal-title":"Diabetologia"},{"issue":"1","key":"1336_CR18","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.survophthal.2022.08.004","volume":"68","author":"MH Shahriari","year":"2023","unstructured":"Shahriari MH, et al. Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: a systematic review. Surv Ophthalmol. 2023;68(1):42\u201353.","journal-title":"Surv Ophthalmol"},{"issue":"7","key":"1336_CR19","doi-asserted-by":"publisher","DOI":"10.2196\/23863","volume":"23","author":"J-H Wu","year":"2021","unstructured":"Wu J-H, et al. Performance and limitation of machine learning algorithms for diabetic retinopathy screening: meta-analysis. J Med Internet Res. 2021;23(7): e23863.","journal-title":"J Med Internet Res"},{"issue":"1","key":"1336_CR20","doi-asserted-by":"publisher","first-page":"23024","DOI":"10.1038\/s41598-021-02479-6","volume":"11","author":"G Ryu","year":"2021","unstructured":"Ryu G, et al. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Sci Rep. 2021;11(1):23024.","journal-title":"Sci Rep"},{"issue":"1","key":"1336_CR21","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s40942-023-00477-6","volume":"9","author":"FM Penha","year":"2023","unstructured":"Penha FM, et al. Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence. Int J Retina Vitreous. 2023;9(1):41.","journal-title":"Int J Retina Vitreous"},{"key":"1336_CR22","doi-asserted-by":"publisher","first-page":"839088","DOI":"10.3389\/fmed.2022.839088","volume":"9","author":"W-F Zhang","year":"2022","unstructured":"Zhang W-F, et al. The validation of deep learning-based grading model for diabetic retinopathy. Front Med. 2022;9:839088.","journal-title":"Front Med"},{"issue":"2","key":"1336_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/1753-0407.13241","volume":"14","author":"N Li","year":"2022","unstructured":"Li N, et al. A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study. J Diabetes. 2022;14(2):111\u201320.","journal-title":"J Diabetes"},{"issue":"12","key":"1336_CR24","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.2337\/dc18-0147","volume":"41","author":"Z Li","year":"2018","unstructured":"Li Z, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509\u201316.","journal-title":"Diabetes Care"},{"key":"1336_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2024.02.012","author":"S Joseph","year":"2024","unstructured":"Joseph S, et al. Diagnostic accuracy of artificial intelligence based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis. Am J Ophthalmol. 2024. https:\/\/doi.org\/10.1016\/j.ajo.2024.02.012.","journal-title":"Am J Ophthalmol"},{"issue":"11","key":"1336_CR26","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.4103\/ijo.IJO_3808_20","volume":"69","author":"A Jain","year":"2021","unstructured":"Jain A, et al. Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy. Indian J Ophthalmol. 2021;69(11):3150\u20134.","journal-title":"Indian J Ophthalmol"},{"issue":"1","key":"1336_CR27","doi-asserted-by":"publisher","first-page":"4330","DOI":"10.1038\/s41598-018-22612-2","volume":"8","author":"S Keel","year":"2018","unstructured":"Keel S, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018;8(1):4330.","journal-title":"Sci Rep"},{"key":"1336_CR28","doi-asserted-by":"crossref","unstructured":"Jiang H, et al. A multi-label deep learning model with interpretable Grad-CAM for diabetic retinopathy classification. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2020.","DOI":"10.1109\/EMBC44109.2020.9175884"},{"issue":"11","key":"1336_CR29","doi-asserted-by":"publisher","first-page":"e2134254","DOI":"10.1001\/jamanetworkopen.2021.34254","volume":"4","author":"E Ipp","year":"2021","unstructured":"Ipp E, et al. Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open. 2021;4(11):e2134254\u2013e2134254.","journal-title":"JAMA Netw Open"},{"issue":"4","key":"1336_CR30","doi-asserted-by":"publisher","first-page":"493","DOI":"10.4067\/s0034-98872021000400493","volume":"149","author":"MC Ib\u00e1\u00f1ez-Bruron","year":"2021","unstructured":"Ib\u00e1\u00f1ez-Bruron MC, et al. Accuracy of artificial intelligence compared to trained medical technologists in diabetic retinopathy screening. Rev Med Chil. 2021;149(4):493\u2013500.","journal-title":"Rev Med Chil"},{"issue":"9","key":"1336_CR31","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.3390\/electronics9091444","volume":"9","author":"Y-P Huang","year":"2020","unstructured":"Huang Y-P, et al. Deep learning models for automated diagnosis of retinopathy of prematurity in preterm infants. Electronics. 2020;9(9):1444.","journal-title":"Electronics"},{"issue":"9","key":"1336_CR32","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1167\/tvst.10.9.18","volume":"10","author":"M-Y Hsu","year":"2021","unstructured":"Hsu M-Y, et al. Deep learning for automated diabetic retinopathy screening fused with heterogeneous data from EHRs can lead to earlier referral decisions. Transl Vis Sci Technol. 2021;10(9):18\u201318.","journal-title":"Transl Vis Sci Technol"},{"issue":"1","key":"1336_CR33","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.jfma.2020.03.024","volume":"120","author":"Y-T Hsieh","year":"2021","unstructured":"Hsieh Y-T, et al. Application of deep learning image assessment software VeriSee\u2122 for diabetic retinopathy screening. J Formos Med Assoc. 2021;120(1):165\u201371.","journal-title":"J Formos Med Assoc"},{"issue":"5","key":"1336_CR34","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1136\/bjophthalmol-2020-316594","volume":"105","author":"P Heydon","year":"2021","unstructured":"Heydon P, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2021;105(5):723\u20138.","journal-title":"Br J Ophthalmol"},{"issue":"3","key":"1336_CR35","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1038\/s41433-019-0562-4","volume":"34","author":"J He","year":"2020","unstructured":"He J, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye. 2020;34(3):572\u20136.","journal-title":"Eye"},{"key":"1336_CR36","doi-asserted-by":"publisher","first-page":"204062232210973","DOI":"10.1177\/20406223221097335","volume":"13","author":"Z Hao","year":"2022","unstructured":"Hao Z, et al. Application and observation of artificial intelligence in clinical practice of fundus screening for diabetic retinopathy with non-mydriatic fundus photography: a retrospective observational study of T2DM patients in Tianjin, China. Ther Adv Chronic Dis. 2022;13:20406223221097336.","journal-title":"Ther Adv Chronic Dis"},{"issue":"2","key":"1336_CR37","doi-asserted-by":"publisher","first-page":"100027","DOI":"10.1016\/j.xops.2021.100027","volume":"1","author":"Y Guo","year":"2021","unstructured":"Guo Y, et al. Quantification of nonperfusion area in montaged widefield OCT angiography using deep learning in diabetic retinopathy. Ophthalmol Sci. 2021;1(2):100027.","journal-title":"Ophthalmol Sci"},{"issue":"9","key":"1336_CR38","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1001\/jamaophthalmol.2019.2004","volume":"137","author":"V Gulshan","year":"2019","unstructured":"Gulshan V, et al. Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmol. 2019;137(9):987\u201393.","journal-title":"JAMA Ophthalmol"},{"issue":"22","key":"1336_CR39","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402\u201310.","journal-title":"JAMA"},{"issue":"11","key":"1336_CR40","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.3390\/jcm10112352","volume":"10","author":"A Grzybowski","year":"2021","unstructured":"Grzybowski A, Brona P. Analysis and comparison of two artificial intelligence diabetic retinopathy screening algorithms in a pilot study: IDx-DR and retinalyze. J Clin Med. 2021;10(11):2352.","journal-title":"J Clin Med"},{"issue":"10","key":"1336_CR41","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/MC.2020.3004392","volume":"53","author":"G Gonzalez-Briceno","year":"2020","unstructured":"Gonzalez-Briceno G, et al. Artificial intelligence-based referral system for patients with diabetic retinopathy. Computer. 2020;53(10):77\u201387.","journal-title":"Computer"},{"issue":"7","key":"1336_CR42","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","volume":"124","author":"R Gargeya","year":"2017","unstructured":"Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124(7):962\u20139.","journal-title":"Ophthalmology"},{"issue":"9","key":"1336_CR43","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1167\/tvst.11.9.34","volume":"11","author":"SL Glinton","year":"2022","unstructured":"Glinton SL, et al. Phenotyping of ABCA4 retinopathy by machine learning analysis of full-field electroretinography. Transl Vis Sci Technol. 2022;11(9):34\u201334.","journal-title":"Transl Vis Sci Technol"},{"issue":"2","key":"1336_CR44","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3390\/electronics9020274","volume":"9","author":"TR Gadekallu","year":"2020","unstructured":"Gadekallu TR, et al. Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics. 2020;9(2):274.","journal-title":"Electronics"},{"key":"1336_CR45","doi-asserted-by":"publisher","DOI":"10.1136\/bjo-2023-323395","author":"AD Fleming","year":"2023","unstructured":"Fleming AD, et al. Deep learning detection of diabetic retinopathy in Scotland\u2019s diabetic eye screening programme. Br J Ophthalmol. 2023. https:\/\/doi.org\/10.1136\/bjo-2023-323395.","journal-title":"Br J Ophthalmol"},{"issue":"13","key":"1336_CR46","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3991\/ijoe.v18i13.33985","volume":"18","author":"OM Al-hazaimeh","year":"2022","unstructured":"Al-hazaimeh OM, et al. Combining artificial intelligence and image processing for diagnosing diabetic retinopathy in retinal fundus images. Int J Online Biomed Eng. 2022;18(13):131.","journal-title":"Int J Online Biomed Eng"},{"issue":"1","key":"1336_CR47","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1038\/s41598-023-28680-3","volume":"13","author":"T Aziz","year":"2023","unstructured":"Aziz T, Charoenlarpnopparut C, Mahapakulchai S. Deep learning-based hemorrhage detection for diabetic retinopathy screening. Sci Rep. 2023;13(1):1479.","journal-title":"Sci Rep"},{"key":"1336_CR48","doi-asserted-by":"publisher","first-page":"205520762311949","DOI":"10.1177\/20552076231194942","volume":"9","author":"G Alwakid","year":"2023","unstructured":"Alwakid G, et al. Deep learning-enhanced diabetic retinopathy image classification. Digit Health. 2023;9:20552076231194944.","journal-title":"Digit Health"},{"issue":"7","key":"1336_CR49","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1007\/s11517-022-02564-6","volume":"60","author":"E AbdelMaksoud","year":"2022","unstructured":"AbdelMaksoud E, Barakat S, Elmogy M. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Med Biol Eng Comput. 2022;60(7):2015\u201338.","journal-title":"Med Biol Eng Comput"},{"issue":"8","key":"1336_CR50","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3390\/diagnostics11081385","volume":"11","author":"M Baget-Bernaldiz","year":"2021","unstructured":"Baget-Bernaldiz M, et al. Testing a deep learning algorithm for detection of diabetic retinopathy in a Spanish diabetic population and with messidor database. Diagnostics. 2021;11(8):1385.","journal-title":"Diagnostics"},{"issue":"7","key":"1336_CR51","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.3390\/sym14071427","volume":"14","author":"A Bilal","year":"2022","unstructured":"Bilal A, et al. AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning. Symmetry. 2022;14(7):1427.","journal-title":"Symmetry"},{"key":"1336_CR52","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11010097","author":"U Bhimavarapu","year":"2022","unstructured":"Bhimavarapu U, Battineni G. Deep learning for the detection and classification of diabetic retinopathy with an improved activation function. Healthcare. 2022. https:\/\/doi.org\/10.3390\/healthcare11010097.","journal-title":"Healthcare"},{"issue":"11","key":"1336_CR53","doi-asserted-by":"publisher","first-page":"3704","DOI":"10.3390\/s21113704","volume":"21","author":"WL Alyoubi","year":"2021","unstructured":"Alyoubi WL, Abulkhair MF, Shalash WM. Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors. 2021;21(11):3704.","journal-title":"Sensors"},{"key":"1336_CR54","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5441366","author":"Y Miao","year":"2022","unstructured":"Miao Y, Tang S. Classification of diabetic retinopathy based on multiscale hybrid attention mechanism and residual algorithm. Wirel Commun Mob Comput. 2022. https:\/\/doi.org\/10.1155\/2022\/5441366.","journal-title":"Wirel Commun Mob Comput"},{"issue":"5","key":"1336_CR55","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.2337\/dc20-1877","volume":"44","author":"AY Lee","year":"2021","unstructured":"Lee AY, et al. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care. 2021;44(5):1168\u201375.","journal-title":"Diabetes Care"},{"key":"1336_CR56","unstructured":"Lam C, et al. Automated detection of diabetic retinopathy using deep learning. In: AMIA Summits on Translational Science Proceedings, 2018. 2018; p. 147."},{"key":"1336_CR57","doi-asserted-by":"crossref","unstructured":"Nugroho HA, Frannita EL. Intelligent diabetic retinopathy detection using deep learning. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE; 2021.","DOI":"10.1109\/ISRITI54043.2021.9702859"},{"issue":"2","key":"1336_CR58","doi-asserted-by":"publisher","first-page":"540","DOI":"10.3390\/diagnostics12020540","volume":"12","author":"GU Nneji","year":"2022","unstructured":"Nneji GU, et al. Identification of diabetic retinopathy using weighted fusion deep learning based on dual-channel fundus scans. Diagnostics. 2022;12(2):540.","journal-title":"Diagnostics"},{"key":"1336_CR59","doi-asserted-by":"publisher","first-page":"773881","DOI":"10.3389\/fmed.2021.773881","volume":"8","author":"C Yang","year":"2021","unstructured":"Yang C, et al. Usefulness of machine learning for identification of referable diabetic retinopathy in a large-scale population-based study. Front Med. 2021;8:773881.","journal-title":"Front Med"},{"key":"1336_CR60","doi-asserted-by":"publisher","first-page":"876559","DOI":"10.3389\/fendo.2022.876559","volume":"13","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, et al. Using machine learning techniques to develop risk prediction models for the risk of incident diabetic retinopathy among patients with type 2 diabetes mellitus: a cohort study. Front Endocrinol. 2022;13:876559.","journal-title":"Front Endocrinol"},{"issue":"16","key":"1336_CR61","doi-asserted-by":"publisher","first-page":"2564","DOI":"10.3390\/electronics11162564","volume":"11","author":"G Pinedo-Diaz","year":"2022","unstructured":"Pinedo-Diaz G, et al. Suitability classification of retinal fundus images for diabetic retinopathy using deep learning. Electronics. 2022;11(16):2564.","journal-title":"Electronics"},{"issue":"8","key":"1336_CR62","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.4103\/IJO.IJO_11_23","volume":"71","author":"J Surya","year":"2023","unstructured":"Surya J, et al. Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma. Indian J Ophthalmol. 2023;71(8):3039\u201345.","journal-title":"Indian J Ophthalmol"},{"issue":"1","key":"1336_CR63","doi-asserted-by":"publisher","DOI":"10.1136\/bmjdrc-2020-001596","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, et al. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care. 2020;8(1): e001596.","journal-title":"BMJ Open Diabetes Res Care"},{"issue":"1","key":"1336_CR64","doi-asserted-by":"publisher","DOI":"10.1136\/bmjdrc-2019-000892","volume":"8","author":"B Sosale","year":"2020","unstructured":"Sosale B, et al. Simple, mobile-based artificial intelligence algorithm in the detection of diabetic retinopathy (SMART) study. BMJ Open Diabetes Res Care. 2020;8(1): e000892.","journal-title":"BMJ Open Diabetes Res Care"},{"issue":"12","key":"1336_CR65","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.3390\/diagnostics12123084","volume":"12","author":"A Mehboob","year":"2022","unstructured":"Mehboob A, et al. A deep learning based approach for grading of diabetic retinopathy using large fundus image dataset. Diagnostics. 2022;12(12):3084.","journal-title":"Diagnostics"},{"issue":"9","key":"1336_CR66","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.3390\/diagnostics12092262","volume":"12","author":"K Mujeeb Rahman","year":"2022","unstructured":"Mujeeb Rahman K, Nasor M, Imran A. Automatic screening of diabetic retinopathy using fundus images and machine learning algorithms. Diagnostics. 2022;12(9):2262.","journal-title":"Diagnostics"},{"issue":"13","key":"1336_CR67","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff MD, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200\u20136.","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"1336_CR68","doi-asserted-by":"publisher","first-page":"27590","DOI":"10.1109\/ACCESS.2023.3257988","volume":"11","author":"T Palaniswamy","year":"2023","unstructured":"Palaniswamy T, Vellingiri M. Internet of things and deep learning enabled diabetic retinopathy diagnosis using retinal fundus images. IEEE Access. 2023;11:27590\u2013601.","journal-title":"IEEE Access"},{"issue":"22","key":"1336_CR69","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting DSW, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211\u201323.","journal-title":"JAMA"},{"key":"1336_CR70","doi-asserted-by":"publisher","first-page":"163328","DOI":"10.1016\/j.ijleo.2019.163328","volume":"199","author":"TJ Jebaseeli","year":"2019","unstructured":"Jebaseeli TJ, Durai CAD, Peter JD. Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM. Optik. 2019;199:163328.","journal-title":"Optik"},{"key":"1336_CR71","doi-asserted-by":"publisher","DOI":"10.31449\/inf.v46i2.3906","author":"M Jena","year":"2022","unstructured":"Jena M, et al. Exploring the parametric impact on a deep learning model and proposal of a 2-branch CNN for diabetic retinopathy classification with case study in IoT-Blockchain based smart healthcare system. Informatica. 2022. https:\/\/doi.org\/10.31449\/inf.v46i2.3906.","journal-title":"Informatica"},{"key":"1336_CR72","doi-asserted-by":"crossref","unstructured":"Jiang H, et al. An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). 2019; IEEE.","DOI":"10.1109\/EMBC.2019.8857160"},{"issue":"4","key":"1336_CR73","doi-asserted-by":"publisher","first-page":"413","DOI":"10.3390\/bioengineering10040413","volume":"10","author":"MB Khan","year":"2023","unstructured":"Khan MB, et al. Automated diagnosis of diabetic retinopathy using deep learning: on the search of segmented retinal blood vessel images for better performance. Bioengineering. 2023;10(4):413.","journal-title":"Bioengineering"},{"key":"1336_CR74","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.patrec.2020.02.026","volume":"133","author":"K Shankar","year":"2020","unstructured":"Shankar K, et al. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett. 2020;133:210\u20136.","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"1336_CR75","doi-asserted-by":"publisher","first-page":"117","DOI":"10.17762\/ijritcc.v11i1.6058","volume":"11","author":"S Kuna","year":"2023","unstructured":"Kuna S, Prasad A. Deep learning empowered diabetic retinopathy detection and classification using retinal fundus images. Int J Recent Innov Trends Comput Commun. 2023;11(1):117\u201327.","journal-title":"Int J Recent Innov Trends Comput Commun"},{"issue":"2","key":"1336_CR76","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1167\/tvst.9.2.60","volume":"9","author":"CA Ludwig","year":"2020","unstructured":"Ludwig CA, et al. Automatic identification of referral-warranted diabetic retinopathy using deep learning on mobile phone images. Transl Vis Sci Technol. 2020;9(2):60\u201360.","journal-title":"Transl Vis Sci Technol"},{"issue":"2","key":"1336_CR77","doi-asserted-by":"publisher","first-page":"391","DOI":"10.4103\/ijo.IJO_1203_19","volume":"68","author":"B Sosale","year":"2020","unstructured":"Sosale B, et al. Medios\u2014an offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Indian J Ophthalmol. 2020;68(2):391\u20135.","journal-title":"Indian J Ophthalmol"},{"key":"1336_CR78","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-981-15-1041-0_6","volume-title":"Algorithms in machine learning paradigms","author":"ND Roy","year":"2020","unstructured":"Roy ND, Biswas A. Deep learning-based early sign detection model for proliferative diabetic retinopathy in neovascularization at the disc. In: Mandal JK, Mukhopadhyay S, Dutta P, Dasgupta K, editors. Algorithms in machine learning paradigms. Berlin: Springer; 2020. p. 91\u2013108."},{"issue":"8","key":"1336_CR79","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1089\/tmj.2019.0137","volume":"26","author":"P Romero-Aroca","year":"2020","unstructured":"Romero-Aroca P, et al. Validation of a deep learning algorithm for diabetic retinopathy. Telemed e-Health. 2020;26(8):1001\u20139.","journal-title":"Telemed e-Health"},{"key":"1336_CR80","doi-asserted-by":"publisher","first-page":"109190","DOI":"10.1016\/j.diabres.2022.109190","volume":"184","author":"X Pei","year":"2022","unstructured":"Pei X, et al. Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. Diabetes Res Clin Pract. 2022;184:109190.","journal-title":"Diabetes Res Clin Pract"},{"issue":"6","key":"1336_CR81","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1080\/13682199.2023.2168851","volume":"70","author":"P Rayavel","year":"2022","unstructured":"Rayavel P, Murukesh C. Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection. Imaging Sci J. 2022;70(6):358\u201370.","journal-title":"Imaging Sci J"},{"key":"1336_CR82","doi-asserted-by":"crossref","unstructured":"Paradisa RH, et al. Classification of diabetic retinopathy through deep feature extraction and classic machine learning approach. In: 2020 3rd International Conference on Information and Communications Technology (ICOIACT). IEEE; 2020.","DOI":"10.1109\/ICOIACT50329.2020.9332082"},{"issue":"1","key":"1336_CR83","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1038\/s41387-022-00216-0","volume":"12","author":"J Li","year":"2022","unstructured":"Li J, et al. Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study. Nutr Diabetes. 2022;12(1):36.","journal-title":"Nutr Diabetes"},{"issue":"5","key":"1336_CR84","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/JBHI.2013.2294635","volume":"18","author":"S Roychowdhury","year":"2013","unstructured":"Roychowdhury S, Koozekanani DD, Parhi KK. DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform. 2013;18(5):1717\u201328.","journal-title":"IEEE J Biomed Health Inform"},{"key":"1336_CR85","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1007\/s00417-020-04853-y","volume":"258","author":"V Sarao","year":"2020","unstructured":"Sarao V, Veritti D, Lanzetta P. Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study. Graefes Arch Clin Exp Ophthalmol. 2020;258:2647\u201354.","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"issue":"11","key":"1336_CR86","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-050989","volume":"11","author":"W Li","year":"2021","unstructured":"Li W, et al. Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China. BMJ Open. 2021;11(11): e050989.","journal-title":"BMJ Open"},{"issue":"6","key":"1336_CR87","doi-asserted-by":"publisher","first-page":"e2217447","DOI":"10.1001\/jamanetworkopen.2022.17447","volume":"5","author":"Q Wu","year":"2022","unstructured":"Wu Q, et al. Development and validation of a deep learning model to predict the occurrence and severity of retinopathy of prematurity. JAMA Netw Open. 2022;5(6):e2217447\u2013e2217447.","journal-title":"JAMA Netw Open"},{"issue":"4","key":"1336_CR88","doi-asserted-by":"publisher","first-page":"e235","DOI":"10.1016\/S2589-7500(22)00017-6","volume":"4","author":"P Ruamviboonsuk","year":"2022","unstructured":"Ruamviboonsuk P, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digital Health. 2022;4(4):e235\u201344.","journal-title":"Lancet Digital Health"},{"issue":"1","key":"1336_CR89","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41746-019-0099-8","volume":"2","author":"P Ruamviboonsuk","year":"2019","unstructured":"Ruamviboonsuk P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2(1):25.","journal-title":"NPJ Digit Med"},{"key":"1336_CR90","first-page":"100022","volume":"3","author":"G Saxena","year":"2020","unstructured":"Saxena G, et al. Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets. Intell-Based Med. 2020;3:100022.","journal-title":"Intell-Based Med"},{"issue":"4","key":"1336_CR91","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.ophtha.2018.11.016","volume":"126","author":"R Sayres","year":"2019","unstructured":"Sayres R, et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology. 2019;126(4):552\u201364.","journal-title":"Ophthalmology"},{"issue":"3","key":"1336_CR92","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1177\/1932296820906212","volume":"15","author":"A Shah","year":"2021","unstructured":"Shah A, et al. Validation of automated screening for referable diabetic retinopathy with an autonomous diagnostic artificial intelligence system in a Spanish population. J Diabetes Sci Technol. 2021;15(3):655\u201363.","journal-title":"J Diabetes Sci Technol"},{"issue":"2","key":"1336_CR93","doi-asserted-by":"publisher","first-page":"398","DOI":"10.4103\/ijo.IJO_966_19","volume":"68","author":"P Shah","year":"2020","unstructured":"Shah P, et al. Validation of deep convolutional neural network-based algorithm for detection of diabetic retinopathy\u2014artificial intelligence versus clinician for screening. Indian J Ophthalmol. 2020;68(2):398\u2013405.","journal-title":"Indian J Ophthalmol"},{"issue":"6","key":"1336_CR94","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","volume":"32","author":"R Rajalakshmi","year":"2018","unstructured":"Rajalakshmi R, et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye. 2018;32(6):1138\u201344.","journal-title":"Eye"},{"issue":"2","key":"1336_CR95","first-page":"175","volume":"15","author":"YMS Reddy","year":"2022","unstructured":"Reddy YMS, Ravindran RSE. Retinal image lesions assisted diabetic retinopathy screening system through machine learning. Int J Intell Eng Syst. 2022;15(2):175.","journal-title":"Int J Intell Eng Syst"},{"issue":"1","key":"1336_CR96","doi-asserted-by":"publisher","DOI":"10.1136\/bmjophth-2022-001140","volume":"7","author":"Y Rom","year":"2022","unstructured":"Rom Y, et al. Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging. BMJ Open Ophthalmol. 2022;7(1): e001140.","journal-title":"BMJ Open Ophthalmol"},{"issue":"2","key":"1336_CR97","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1038\/s41433-020-0927-8","volume":"35","author":"TW Rogers","year":"2021","unstructured":"Rogers TW, et al. Evaluation of an AI system for the detection of diabetic retinopathy from images captured with a handheld portable fundus camera: the MAILOR AI study. Eye. 2021;35(2):632\u20138.","journal-title":"Eye"},{"issue":"2","key":"1336_CR98","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1167\/tvst.11.2.39","volume":"11","author":"G Ryu","year":"2022","unstructured":"Ryu G, et al. A deep learning algorithm for classifying diabetic retinopathy using optical coherence tomography angiography. Transl Vis Sci Technol. 2022;11(2):39\u201339.","journal-title":"Transl Vis Sci Technol"}],"container-title":["BioMedical Engineering OnLine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-025-01336-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12938-025-01336-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-025-01336-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T19:27:45Z","timestamp":1741980465000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedical-engineering-online.biomedcentral.com\/articles\/10.1186\/s12938-025-01336-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":98,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1336"],"URL":"https:\/\/doi.org\/10.1186\/s12938-025-01336-1","relation":{},"ISSN":["1475-925X"],"issn-type":[{"value":"1475-925X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]},"assertion":[{"value":"18 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"34"}}