{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T03:14:07Z","timestamp":1789010047462,"version":"build-2803163510"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017266","name":"Navarra Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.compmedimag.2026.102771","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:03:10Z","timestamp":1776873790000},"page":"102771","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["AI-driven retinal image quality monitoring in diabetic retinopathy screening: A retrospective study identifying actionable insights to improve imaging protocols"],"prefix":"10.1016","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5687-2807","authenticated-orcid":false,"given":"Imanol","family":"Pinto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1lvaro","family":"Olazar\u00e1n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Jurio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borja","family":"de la Osa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Sainz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aritz","family":"Oscoz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jer\u00f3nimo","family":"Ballaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Gorricho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikel","family":"Galar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Andonegui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2026.102771_b1","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning","volume":"57","author":"Abr\u00e0moff","year":"2016","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"10.1016\/j.compmedimag.2026.102771_b2","series-title":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems","first-page":"1","article-title":"A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy","author":"Beede","year":"2020"},{"key":"10.1016\/j.compmedimag.2026.102771_b3","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1089\/dia.2019.0164","article-title":"The value of automated diabetic retinopathy screening with the EyeArt system: A study of more than 100,000 consecutive encounters from people with diabetes","volume":"21","author":"Bhaskaranand","year":"2019","journal-title":"Diabetes Technol. Ther."},{"key":"10.1016\/j.compmedimag.2026.102771_b4","first-page":"233","article-title":"The relationship between precision-recall and ROC curves","volume":"148","author":"Davis","year":"2006","journal-title":"ACM Int. Conf. Proceeding Ser."},{"key":"10.1016\/j.compmedimag.2026.102771_b5","series-title":"EyeArt official web page","author":"EyeArt","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b6","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2337\/diacare.26.2007.S99","article-title":"Diabetic retinopathy","volume":"26","author":"Fong","year":"2003","journal-title":"Diabetes Care"},{"key":"10.1016\/j.compmedimag.2026.102771_b7","first-page":"48","article-title":"Evaluation of retinal image quality assessment networks in different color-spaces","volume":"11764 LNCS","author":"Fu","year":"2019","journal-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)"},{"key":"10.1016\/j.compmedimag.2026.102771_b8","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.compmedimag.2026.102771_b9","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1001\/jamaophthalmol.2019.2004","article-title":"Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India","volume":"137","author":"Gulshan","year":"2019","journal-title":"JAMA Ophthalmol."},{"key":"10.1016\/j.compmedimag.2026.102771_b10","doi-asserted-by":"crossref","DOI":"10.1177\/20406223221097335","article-title":"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","volume":"13","author":"Hao","year":"2022","journal-title":"Ther. Adv. Chronic Dis."},{"key":"10.1016\/j.compmedimag.2026.102771_b11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/aos.13613","article-title":"Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the hoorn diabetes care system","volume":"96","author":"van der Heijden","year":"2018","journal-title":"Acta Ophthalmol."},{"key":"10.1016\/j.compmedimag.2026.102771_b12","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1136\/bjophthalmol-2020-316594","article-title":"Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30000 patients","volume":"105","author":"Heydon","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2026.102771_b13","series-title":"Which image models are best","author":"Howard","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b14","article-title":"Fastai: A layered API for deep learning","volume":"11","author":"Howard","year":"2020","journal-title":"Inf. (Switzerland)"},{"key":"10.1016\/j.compmedimag.2026.102771_b15","series-title":"IDx-dr official web page","author":"IDx-DR","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b16","series-title":"Diabetic retinopathy detection","author":"Kaggle","year":"2016"},{"key":"10.1016\/j.compmedimag.2026.102771_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-024-46142-w","article-title":"Empirical data drift detection experiments on real-world medical imaging data","volume":"15","author":"Kore","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compmedimag.2026.102771_b18","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.ophtha.2018.01.034","article-title":"Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy","volume":"125","author":"Krause","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2026.102771_b19","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.2337\/dc20-1877","article-title":"Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems","volume":"44","author":"Lee","year":"2021","journal-title":"Diabetes Care"},{"key":"10.1016\/j.compmedimag.2026.102771_b20","doi-asserted-by":"crossref","first-page":"16173","DOI":"10.1007\/s11042-019-07751-6","article-title":"Retinal image quality assessment for diabetic retinopathy screening: A survey","volume":"79","author":"Lin","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compmedimag.2026.102771_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103088","article-title":"Diffusion models for out-of-distribution detection in digital pathology","volume":"93","author":"Linmans","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2026.102771_b22","article-title":"DeepDRiD: Diabetic retinopathy\u2014Grading and image quality estimation challenge","author":"Liu","year":"2022","journal-title":"Patterns"},{"key":"10.1016\/j.compmedimag.2026.102771_b23","first-page":"326","article-title":"CheXstray: A real-time multi-modal monitoring workflow for medical imaging AI","volume":"14222 LNCS","author":"Merkow","year":"2023","journal-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)"},{"key":"10.1016\/j.compmedimag.2026.102771_b24","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","article-title":"A unifying view on dataset shift in classification","volume":"45","author":"Moreno-Torres","year":"2012","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2026.102771_b25","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2025.1547045","article-title":"Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development","volume":"7","author":"Pinto","year":"2025","journal-title":"Front. Digit. Health"},{"key":"10.1016\/j.compmedimag.2026.102771_b26","series-title":"Retmarker official web page","author":"Retmarker","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b27","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1159\/000368426","article-title":"Screening for diabetic retinopathy in the central region of Portugal. Added value of automated \u2019disease\/no disease\u2019 grading","volume":"233","author":"Ribeiro","year":"2014","journal-title":"Ophthalmologica"},{"key":"10.1016\/j.compmedimag.2026.102771_b28","article-title":"Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program","volume":"2","author":"Ruamviboonsuk","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.compmedimag.2026.102771_b29","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20220878","article-title":"Data drift in medical machine learning: implications and potential remedies","volume":"96","author":"Sahiner","year":"2023","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.compmedimag.2026.102771_b30","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.2337\/diacare.28.10.2448","article-title":"The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening","volume":"28","author":"Scanlon","year":"2005","journal-title":"Diabetes Care"},{"key":"10.1016\/j.compmedimag.2026.102771_b31","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.diabet.2012.05.003","article-title":"Ophdiat\u00ae: Five-year experience of a telemedical screening programme for diabetic retinopathy in Paris and the surrounding area","volume":"38","author":"Schulze-D\u00f6bold","year":"2012","journal-title":"Diabetes Metab."},{"key":"10.1016\/j.compmedimag.2026.102771_b32","series-title":"SELENA+ official web page","author":"SELENA+","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b33","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.19.4.046006","article-title":"Identification of suitable fundus images using automated quality assessment methods","volume":"19","author":"Sevik","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"10.1016\/j.compmedimag.2026.102771_b34","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1177\/1932296820906212","article-title":"Validation of automated screening for referable diabetic retinopathy with an autonomous diagnostic artificial intelligence system in a spanish population","volume":"15","author":"Shah","year":"2020","journal-title":"J. Diabetes Sci. Technol."},{"key":"10.1016\/j.compmedimag.2026.102771_b35","series-title":"Models for pytorch (fast.ai docs)","author":"Timm","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102771_b36","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales","volume":"110","author":"Wilkinson","year":"2003","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2026.102771_b37","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: The international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2026.102771_b38","doi-asserted-by":"crossref","DOI":"10.1007\/s10278-024-01212-9","article-title":"Out-of-distribution detection and radiological data monitoring using statistical process control","author":"Zamzmi","year":"2024","journal-title":"J. Imaging Inform. Med."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000741?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000741?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:41:50Z","timestamp":1778762510000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126000741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":38,"alternative-id":["S0895611126000741"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102771","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI-driven retinal image quality monitoring in diabetic retinopathy screening: A retrospective study identifying actionable insights to improve imaging protocols","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102771","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102771"}}