{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:25:01Z","timestamp":1780601101042,"version":"3.54.1"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["2021GXNSFBA220075"],"award-info":[{"award-number":["2021GXNSFBA220075"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62250410370"],"award-info":[{"award-number":["62250410370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bspc.2026.110193","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:14:11Z","timestamp":1774991651000},"page":"110193","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PC","title":["ROPNet: A novel deep learning framework for retinopathy of prematurity detection"],"prefix":"10.1016","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-8254","authenticated-orcid":false,"given":"Idowu Paul","family":"Okuwobi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jifeng","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110193_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2026.103959","article-title":"Fundus image quality assessment in retinopathy of prematurity via multi-label graph evidential network","volume":"110","author":"Wu","year":"2026","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0010","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.oret.2025.07.003","article-title":"Topographic features in retinopathy of prematurity with en face ultra-widefield OCT","volume":"10","author":"Sutter","year":"2026","journal-title":"Ophthalmol. Retina"},{"key":"10.1016\/j.bspc.2026.110193_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.oret.2026.01.003","article-title":"Severe posterior retinopathy of prematurity: a matched cohort analysis","author":"Chaaya","year":"2026","journal-title":"Ophthalmol. Retina"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2025.100912","article-title":"OCT-derived quantitative measurement of extent of vascularization (\u2018zone\u2019) in retinopathy of prematurity","volume":"6","author":"Sutter","year":"2026","journal-title":"Ophthalmol. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0025","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.survophthal.2025.06.007","article-title":"Retinopathy of prematurity training and education: a systematic review","volume":"71","author":"Song","year":"2026","journal-title":"Surv. Ophthalmol."},{"issue":"8","key":"10.1016\/j.bspc.2026.110193_b0030","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1038\/s41433-025-03651-2","article-title":"Prevalence and associated factors for retinopathy of prematurity at a tertiary hospital in Dar es Salaam, Tanzania","volume":"39","author":"Mhina","year":"2025","journal-title":"Eye"},{"key":"10.1016\/j.bspc.2026.110193_b0035","article-title":"Global and regional trends in retinopathy of prematurity","author":"Wong","year":"2025","journal-title":"JAMA Ophthalmol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0040","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s12886-024-03836-5","article-title":"Screening for retinopathy of prematurity in China: a five-year cohort study in seven screening centers","volume":"25","author":"Zhong","year":"2025","journal-title":"BMC Ophthalmol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0045","doi-asserted-by":"crossref","first-page":"40571","DOI":"10.1038\/s41598-025-24303-1","article-title":"Validation of a screening score model to predict the development of retinopathy of prematurity","volume":"15","author":"Siswanto","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110193_b0050","first-page":"Dec","article-title":"Cost analysis of risk stratified retinopathy of prematurity screening","author":"Hwang","year":"2025","journal-title":"Ophthalmol. Retina"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0055","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s40137-025-00468-6","article-title":"Advances in the management and treatment of retinopathy of prematurity","volume":"13","author":"Zhou","year":"2025","journal-title":"Curr. Surg. Rep."},{"key":"10.1016\/j.bspc.2026.110193_b0060","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.2147\/OPTH.S519292","article-title":"Retinopathy of prematurity (ROP): an overview of biomarkers in various samples for prediction, diagnosis, and prognosis","volume":"19","author":"Huang","year":"2025","journal-title":"Clin. Ophthalmol."},{"key":"10.1016\/j.bspc.2026.110193_b0065","article-title":"Retinopathy of prematurity (ROP): from the perspective of the neonatologist","volume":"8","author":"Koc","year":"2024","journal-title":"Glob. Pediatr."},{"issue":"11","key":"10.1016\/j.bspc.2026.110193_b0070","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1002\/sim.9341","article-title":"Robustness of \u03ba\u2010type coefficients for clinical agreement","volume":"41","author":"Vanacore","year":"2022","journal-title":"Stat. Med."},{"issue":"11","key":"10.1016\/j.bspc.2026.110193_b0075","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1016\/j.ophtha.2024.06.006","article-title":"Use of an artificial intelligence-generated vascular severity score improved plus disease diagnosis in retinopathy of prematurity","volume":"131","author":"Coyner","year":"2024","journal-title":"Ophthalmology"},{"issue":"4","key":"10.1016\/j.bspc.2026.110193_b0080","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1001\/jamaophthalmol.2024.0045","article-title":"Multinational external validation of autonomous retinopathy of prematurity screening","volume":"142","author":"Coyner","year":"2024","journal-title":"JAMA Ophthalmol."},{"issue":"6","key":"10.1016\/j.bspc.2026.110193_b0085","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s10462-025-11153-6","article-title":"Current and future roles of artificial intelligence in retinopathy of prematurity","volume":"58","author":"Jafarizadeh","year":"2025","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"10.1016\/j.bspc.2026.110193_b0090","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.survophthal.2024.03.008","article-title":"A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: from possibility to reality","volume":"69","author":"Wu","year":"2024","journal-title":"Surv. Ophthalmol."},{"issue":"11","key":"10.1016\/j.bspc.2026.110193_b0095","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1167\/iovs.66.11.17","article-title":"Foveal identification and development in prematurity\u2013implications on zone localization and nutritional supplementation","volume":"66","author":"Shah","year":"2025","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"10.1016\/j.bspc.2026.110193_b0100","doi-asserted-by":"crossref","unstructured":"S. M. Sharafi, N. Ebrahimiadib, R. Roohipourmoallai, A. D. Farahani, M. I. Fooladi, and E. Khalili Pour, \u201cAutomated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics,\u201d Sci. Rep., vol. 14, no. 1, p. 6375, Mar. 2024, doi: 10.1038\/s41598-024-57072-4.","DOI":"10.1038\/s41598-024-57072-4"},{"key":"10.1016\/j.bspc.2026.110193_b0105","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-026-37064-2","article-title":"Automated diagnosis of plus form and early stages of ROP using deep learning models","author":"Vahidmoghadam","year":"2026","journal-title":"Sci. Rep."},{"issue":"6","key":"10.1016\/j.bspc.2026.110193_b0110","doi-asserted-by":"crossref","first-page":"195","DOI":"10.3892\/br.2025.2073","article-title":"Systematic review and meta-analysis of risk prediction models for retinopathy of prematurity in preterm infants","volume":"23","author":"Li","year":"2025","journal-title":"Biomed. Rep."},{"key":"10.1016\/j.bspc.2026.110193_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2025.103182","article-title":"Classification of stages 1,2,3 and preplus, plus disease of ROP using MultiCNN_LSTM classifier","volume":"14","author":"Agrawal","year":"2025","journal-title":"MethodsX"},{"key":"10.1016\/j.bspc.2026.110193_b0120","doi-asserted-by":"crossref","unstructured":"K. J, S. Bin Khalid, S. Vittal, S. Muralidhar, A. Vinekar, and G. Srinivasa, \u201cAutomated diagnosis of early-stage retinopathy of prematurity,\u201d Eye, vol. 40, no. 1, pp. 134\u2013138, Jan. 2026, doi: 10.1038\/s41433-025-04122-4.","DOI":"10.1038\/s41433-025-04122-4"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0125","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41433-025-04096-3","article-title":"Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment","volume":"40","author":"Shoeibi","year":"2026","journal-title":"Eye"},{"key":"10.1016\/j.bspc.2026.110193_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103723","article-title":"ROP lesion segmentation via sequence coding and block balancing","volume":"105","author":"Jia","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110193_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128069","article-title":"Diagnosis system for retinopathy of prematurity with Fourier parameterized rotation equivariant convolutions network and prompt mechanism","volume":"286","author":"Chen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110193_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107135","article-title":"ROPRNet: deep learning-assisted recurrence prediction for retinopathy of prematurity","volume":"100","author":"Huang","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110193_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111938","article-title":"Automated detection of stages, zones, and plus diseases of Retinopathy of Prematurity using quantum convolutional networks in neonatal fundus images","volume":"160","author":"Vm","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110193_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108950","article-title":"Early detection of retinopathy of prematurity using a CNN-LSTM-attention model: a non-invasive risk classification approach","volume":"113","author":"Bharathy","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110193_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2026.109555","article-title":"Feature intensity-based vision transformer for accurate detection of vitreous hemorrhage in fundus images","volume":"116","author":"Rajkumar","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110193_b0160","doi-asserted-by":"crossref","unstructured":"K. Hemalatha, A. D. Aravindraj, P. R. V. Vardhan, and H. H. S. S., \u201cMutual information clustering and segmentation framework with masking noise few-shot domain adaptation ResNet for multi-modal brain tumor classification,\u201d Biomed. Signal Process. Control, vol. 114, p. 109290, Apr. 2026, doi: 10.1016\/j.bspc.2025.109290.","DOI":"10.1016\/j.bspc.2025.109290"},{"key":"10.1016\/j.bspc.2026.110193_b0165","doi-asserted-by":"crossref","unstructured":"C. Chen, N. A. Mat Isa, X. Liu, J. Ding, and L. Lu, \u201cMSA-Net: multi-scale attention-based DenseNet for multi-label chest X-ray image classification,\u201d Biomed. Signal Process. Control, vol. 113, p. 109069, Mar. 2026, doi: 10.1016\/j.bspc.2025.109069.","DOI":"10.1016\/j.bspc.2025.109069"},{"key":"10.1016\/j.bspc.2026.110193_b0170","series-title":"Intelligent Networked Things","first-page":"207","article-title":"Non-invasive blood glucose detection algorithm based onimproved MobileNet-V3","author":"Sheng","year":"2026"},{"key":"10.1016\/j.bspc.2026.110193_b0175","series-title":"In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC), Mar","first-page":"1598","article-title":"An improved EfficientNet-B0 approach for brain tumor MRI image classification","author":"Li","year":"2025"},{"key":"10.1016\/j.bspc.2026.110193_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2025.103333","article-title":"Parkinson\u2019s disease detection using inceptionV3: a deep learning approach","volume":"14","author":"Shanthappa","year":"2025","journal-title":"MethodsX"},{"key":"10.1016\/j.bspc.2026.110193_b0185","series-title":"Advanced Brain Tumor Classification with UNet Segmentation and Deep Maxout Classifier Techniques","first-page":"337","author":"Maram","year":"2025"},{"key":"10.1016\/j.bspc.2026.110193_b0190","doi-asserted-by":"crossref","unstructured":"S. Sk and A. S, \u201cA dual-stream parallel architecture for robust visual tracking using scale-aware region proposals,\u201d Future Gener. Comput. Syst., vol. 175, p. 108079, Feb. 2026, doi: 10.1016\/j.future.2025.108079.","DOI":"10.1016\/j.future.2025.108079"},{"issue":"6","key":"10.1016\/j.bspc.2026.110193_b0195","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.17447","article-title":"Development and validation of a deep learning model to predict the occurrence and severity of retinopathy of prematurity","volume":"5","author":"Wu","year":"2022","journal-title":"JAMA Netw. Open"},{"issue":"4","key":"10.1016\/j.bspc.2026.110193_b0200","doi-asserted-by":"crossref","DOI":"10.1002\/hsr2.70718","article-title":"Application of artificial intelligence in retinopathy of prematurity from 2010 to 2023: a bibliometric analysis","volume":"8","author":"Gao","year":"2025","journal-title":"Health Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110193_b0205","doi-asserted-by":"crossref","unstructured":"\u201cKruskal-Wallis Test,\u201d in The Concise Encyclopedia of Statistics, Springer, New York, NY, 2008, pp. 288\u2013290. doi: 10.1007\/978-0-387-32833-1_216.","DOI":"10.1007\/978-0-387-32833-1_216"},{"issue":"2","key":"10.1016\/j.bspc.2026.110193_b0210","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1364\/BOE.506119","article-title":"Automatic zoning for retinopathy of prematurity with a key area location system","volume":"15","author":"Peng","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.bspc.2026.110193_b0215","article-title":"Evaluation of Cohen\u2019s kappa and other measures of inter-rater agreement for genre analysis and other nominal data","volume":"53","author":"Rau","year":"2021","journal-title":"J. Engl. Acad. Purp."},{"issue":"1148","key":"10.1016\/j.bspc.2026.110193_b0220","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20220972","article-title":"Interobserver variability studies in diagnostic imaging: a methodological systematic review","volume":"96","author":"Quinn","year":"2023","journal-title":"Br. J. Radiol."},{"issue":"10","key":"10.1016\/j.bspc.2026.110193_b0225","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1140\/epjc\/s10052-025-14900-9","article-title":"Localization of q-form field on squared curvature gravity domain wall brane coupling with gravity and background scalar","volume":"85","author":"Zhang","year":"2025","journal-title":"Eur. Phys. J. C"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0230","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1038\/s41597-024-03897-7","article-title":"FARFUM-RoP, a dataset for computer-aided detection of Retinopathy of Prematurity","volume":"11","author":"Akbari","year":"2024","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0235","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1038\/s41597-024-03409-7","article-title":"Retinal image dataset of infants and retinopathy of prematurity","volume":"11","author":"Timkovi\u010d","year":"2024","journal-title":"Sci. Data"},{"issue":"4","key":"10.1016\/j.bspc.2026.110193_b0240","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1007\/s10278-021-00477-8","article-title":"Assistive framework for automatic detection of all the zones in retinopathy of prematurity using deep learning","volume":"34","author":"Agrawal","year":"2021","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110193_b0245","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1038\/s41598-025-31624-8","article-title":"Macretina: a dataset, to support deep learning assisted retinopathy of prematurity diagnosis","volume":"16","author":"Trivedi","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110193_b0250","doi-asserted-by":"crossref","unstructured":"J. Jacob and R. W. Arnold, \u201cThe characteristics of racial disparity in retinopathy of prematurity outcomes,\u201d J. Perinatol., pp. 1\u20136, Jul. 2025, doi: 10.1038\/s41372-025-02355-5.","DOI":"10.1038\/s41372-025-02355-5"},{"key":"10.1016\/j.bspc.2026.110193_b0255","doi-asserted-by":"crossref","unstructured":"M. M. Li et al., \u201cScaling medical AI across clinical contexts,\u201d Nat. Med., pp. 1\u201310, Feb. 2026, doi: 10.1038\/s41591-025-04184-7.","DOI":"10.1038\/s41591-025-04184-7"},{"key":"10.1016\/j.bspc.2026.110193_b0260","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"issue":"7","key":"10.1016\/j.bspc.2026.110193_b0265","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.ophtha.2020.10.025","article-title":"Evaluation of a deep learning\u2013derived quantitative retinopathy of prematurity severity scale","volume":"128","author":"Campbell","year":"2021","journal-title":"Ophthalmology"},{"issue":"2","key":"10.1016\/j.bspc.2026.110193_b0270","doi-asserted-by":"crossref","first-page":"597","DOI":"10.22237\/jmasm\/1257035100","article-title":"New effect size rules of thumb","volume":"8","author":"Sawilowsky","year":"2009","journal-title":"J. Mod. Appl. Stat. Methods"},{"issue":"4","key":"10.1016\/j.bspc.2026.110193_b0275","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1002\/bimj.200410135","article-title":"Estimation of the Youden index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom. J."},{"issue":"6","key":"10.1016\/j.bspc.2026.110193_b0280","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen","year":"2023","journal-title":"Nat. Biomed. Eng."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007470?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007470?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T03:31:27Z","timestamp":1776223887000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426007470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":56,"alternative-id":["S1746809426007470"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110193","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ROPNet: A novel deep learning framework for retinopathy of prematurity detection","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110193","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110193"}}