{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T08:17:01Z","timestamp":1781597821578,"version":"3.54.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001782","name":"University of Melbourne","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001782","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The NHMRC Investigator Grant","award":["APP1175405"],"award-info":[{"award-number":["APP1175405"]}]},{"name":"The NHMRC Investigator Grant","award":["APP1175405"],"award-info":[{"award-number":["APP1175405"]}]},{"name":"Medical Research Future Fund","award":["MRFAI00035"],"award-info":[{"award-number":["MRFAI00035"]}]},{"name":"Global STEM Professorship Scheme","award":["P0046113"],"award-info":[{"award-number":["P0046113"]}]},{"name":"the Fundamental Research Funds of the State Key Laboratory of Ophthalmology"},{"name":"Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China","award":["Z012014075"],"award-info":[{"award-number":["Z012014075"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01436-1","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T05:55:53Z","timestamp":1740376553000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Real-world feasibility, accuracy and acceptability of automated retinal photography and AI-based cardiovascular disease risk assessment in Australian primary care settings: a pragmatic trial"],"prefix":"10.1038","volume":"8","author":[{"given":"Wenyi","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malcolm","family":"Clark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacqueline","family":"Henwood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianwen","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiye","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katerina","family":"Kiburg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"van Wijngaarden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoting","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"1436_CR1","unstructured":"Australian Institute of Health and Welfare. Heart, stroke and vascular disease: Australian facts. Canberra: AIHW, (2023)."},{"key":"1436_CR2","unstructured":"Cardiovascular diseases: Avoiding heart attacks and strokes. Available at https:\/\/www.who.int\/news-room\/questions-and-answers\/item\/cardiovascular-diseases-avoiding-heart-attacks-and-strokes. Accessed 12 March 2024."},{"key":"1436_CR3","unstructured":"Group WCRCW. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health 7: e1332\u2013e1345 (2019)."},{"key":"1436_CR4","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1161\/01.CIR.97.18.1837","volume":"97","author":"PWF Wilson","year":"1998","unstructured":"Wilson, P. W. F. et al. Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation 97, 1837\u20131847 (1998).","journal-title":"Circulation"},{"key":"1436_CR5","doi-asserted-by":"publisher","first-page":"S49","DOI":"10.1161\/01.cir.0000437741.48606.98","volume":"129","author":"DC Goff","year":"2014","unstructured":"Goff, D. C. et al. 2013 ACC\/AHA Guideline on the Assessment of Cardiovascular Risk. Circulation 129, S49\u2013S73 (2014).","journal-title":"Circulation"},{"key":"1436_CR6","doi-asserted-by":"publisher","first-page":"j2099","DOI":"10.1136\/bmj.j2099","volume":"357","author":"J Hippisley-Cox","year":"2017","unstructured":"Hippisley-Cox, J., Coupland, C. & Brindle, P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357, j2099 (2017).","journal-title":"BMJ"},{"key":"1436_CR7","unstructured":"group Sw, collaboration ECr. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur. Heart J. 42: 2439\u20132454 (2021)."},{"key":"1436_CR8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/1471-2296-13-54","volume":"13","author":"N Stocks","year":"2012","unstructured":"Stocks, N., Allan, J., Frank, O., Williams, S. & Ryan, P. Improving attendance for cardiovascular risk assessment in Australian general practice: an RCT of a monetary incentive for patients. BMC Fam. Pr. 13, 54 (2012).","journal-title":"BMC Fam. Pr."},{"key":"1436_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-021-07310-6","volume":"22","author":"CM Hespe","year":"2022","unstructured":"Hespe, C. M., Giskes, K., Harris, M. F. & Peiris, D. Findings and lessons learnt implementing a cardiovascular disease quality improvement program in Australian primary care: a mixed method evaluation. BMC Health Serv. Res 22, 108 (2022).","journal-title":"BMC Health Serv. Res"},{"key":"1436_CR10","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1093\/eurheartj\/eht023","volume":"34","author":"J Flammer","year":"2013","unstructured":"Flammer, J. et al. The eye and the heart. Eur. Heart J. 34, 1270\u20131278 (2013).","journal-title":"Eur. Heart J."},{"key":"1436_CR11","doi-asserted-by":"publisher","first-page":"404","DOI":"10.7326\/0003-4819-151-6-200909150-00005","volume":"151","author":"K McGeechan","year":"2009","unstructured":"McGeechan, K. et al. Meta-analysis: retinal vessel caliber and risk for coronary heart disease. Ann. Intern Med. 151, 404\u2013413 (2009).","journal-title":"Ann. Intern Med."},{"key":"1436_CR12","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1007\/s00125-021-05499-z","volume":"64","author":"E Sandoval-Garcia","year":"2021","unstructured":"Sandoval-Garcia, E. et al. Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia 64, 2215\u20132227 (2021).","journal-title":"Diabetologia"},{"key":"1436_CR13","doi-asserted-by":"publisher","DOI":"10.1111\/micc.12772","volume":"29","author":"BK Betzler","year":"2022","unstructured":"Betzler, B. K. et al. Retinal vascular profile in predicting incident cardiometabolic diseases among individuals with diabetes. Microcirculation 29, e12772 (2022).","journal-title":"Microcirculation"},{"key":"1436_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1167\/tvst.12.7.14","volume":"12","author":"W Hu","year":"2023","unstructured":"Hu, W. et al. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl. Vis. Sci. Technol. 12, 14 (2023).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"1436_CR15","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s42256-021-00427-7","volume":"4","author":"A Diaz-Pinto","year":"2022","unstructured":"Diaz-Pinto, A. et al. Predicting myocardial infarction through retinal scans and minimal personal information. NATURE MACHINE INTELLIGENCE 4, 55 (2022).","journal-title":"NATURE MACHINE INTELLIGENCE"},{"key":"1436_CR16","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cvdhj.2023.12.004","volume":"2024","author":"E Vaghefi","year":"2024","unstructured":"Vaghefi, E. et al. Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets. Cardiovasc. Digit. Health J. 2024, 59\u201369 (2024).","journal-title":"Cardiovasc. Digit. Health J."},{"key":"1436_CR17","unstructured":"Friso, G. H. et al. Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from the Maastricht study. ProcSPIE; 2020; (2020)."},{"key":"1436_CR18","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1038\/s41598-020-61519-9","volume":"10","author":"YD Kim","year":"2020","unstructured":"Kim, Y. D. et al. Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Sci. Rep. 10, 4623 (2020).","journal-title":"Sci. Rep."},{"key":"1436_CR19","doi-asserted-by":"publisher","first-page":"7180","DOI":"10.1038\/s41598-019-43670-0","volume":"9","author":"E Vaghefi","year":"2019","unstructured":"Vaghefi, E. et al. Detection of smoking status from retinal images; a Convolutional Neural Network study. Sci. Rep. 9, 7180 (2019).","journal-title":"Sci. Rep."},{"key":"1436_CR20","doi-asserted-by":"publisher","first-page":"e306","DOI":"10.1016\/S2589-7500(21)00043-1","volume":"3","author":"TH Rim","year":"2021","unstructured":"Rim, T. H. et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health 3, e306\u2013e316 (2021).","journal-title":"Lancet Digit Health"},{"key":"1436_CR21","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158\u2013164 (2018).","journal-title":"Nat. Biomed. Eng."},{"key":"1436_CR22","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 Digit Health 4, e235\u2013e244 (2022).","journal-title":"Lancet Digit Health"},{"key":"1436_CR23","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.scib.2021.08.016","volume":"67","author":"Y Ma","year":"2022","unstructured":"Ma, Y. et al. Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in China. Sci. Bull. 67, 17\u201320 (2022).","journal-title":"Sci. Bull."},{"key":"1436_CR24","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1093\/ehjdh\/ztad023","volume":"4","author":"JK Yi","year":"2023","unstructured":"Yi, J. K. et al. Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores. Eur. Heart J. Digit Health 4, 236\u2013244 (2023).","journal-title":"Eur. Heart J. Digit Health"},{"key":"1436_CR25","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1093\/jamia\/ocad199","volume":"31","author":"CJ Lee","year":"2023","unstructured":"Lee, C. J. et al. Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI. J. Am. Med Inf. Assoc. 31, 130\u2013138 (2023).","journal-title":"J. Am. Med Inf. Assoc."},{"key":"1436_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-022-02684-8","volume":"21","author":"R Tseng","year":"2023","unstructured":"Tseng, R. et al. Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank. BMC Med. 21, 28 (2023).","journal-title":"BMC Med."},{"key":"1436_CR27","unstructured":"Commonwealth of Australia as represented by the Department of Health and Aged Care. Australian Guideline for assessing and managing cardiovascular disease risk. (2023)."},{"key":"1436_CR28","doi-asserted-by":"publisher","first-page":"750","DOI":"10.47102\/annals-acadmedsg.V39N10p750","volume":"39","author":"LE Wee","year":"2010","unstructured":"Wee, L. E., Koh, G. C. & Toh, Z. J. Multi-disease health screening in an urban low-income setting: a community-based study. Ann. Acad. Med Singap. 39, 750\u2013757 (2010).","journal-title":"Ann. Acad. Med Singap."},{"key":"1436_CR29","first-page":"U2312","volume":"119","author":"G Sinclair","year":"2006","unstructured":"Sinclair, G. & Kerr, A. The Bold Promise Project: a system change in primary care to support cardiovascular risk screening. N. Z. Med J. 119, U2312 (2006).","journal-title":"N. Z. Med J."},{"key":"1436_CR30","doi-asserted-by":"publisher","first-page":"320","DOI":"10.5694\/mja15.01004","volume":"204","author":"E Banks","year":"2016","unstructured":"Banks, E. et al. Absolute risk of cardiovascular disease events, and blood pressure- and lipid-lowering therapy in Australia. Med J. Aust. 204, 320 (2016).","journal-title":"Med J. Aust."},{"key":"1436_CR31","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-023-01961-1","volume":"23","author":"A Dehghan","year":"2023","unstructured":"Dehghan, A., Rayatinejad, A., Khezri, R., Aune, D. & Rezaei, F. Laboratory-based versus non-laboratory-based World Health Organization risk equations for assessment of cardiovascular disease risk. BMC Med. Res. Methodol. 23, 141 (2023).","journal-title":"BMC Med. Res. Methodol."},{"key":"1436_CR32","doi-asserted-by":"crossref","unstructured":"Fu, H. et al. Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces. 2019; Cham: Springer International Publishing; 2019.","DOI":"10.1007\/978-3-030-32239-7_6"},{"key":"1436_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.scib.2021.08.016","volume":"67","author":"Y Ma","year":"2022","unstructured":"Ma, Y. et al. Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in China. Sci. Bull. (Beijing) 67, 17\u201320 (2022).","journal-title":"Sci. Bull. (Beijing)"},{"key":"1436_CR34","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.ajo.2020.03.027","volume":"217","author":"J Chang","year":"2020","unstructured":"Chang, J. et al. Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images. Am. J. Ophthalmol.217, 121\u2013130 (2020).","journal-title":"Am. J. Ophthalmol."},{"key":"1436_CR35","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015).","journal-title":"PLoS Med"},{"key":"1436_CR36","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1177\/096228029900800204","volume":"8","author":"JM Bland","year":"1999","unstructured":"Bland, J. M. & Altman, D. G. Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8, 135\u2013160 (1999).","journal-title":"Stat. Methods Med. Res."},{"key":"1436_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101366","volume":"42","author":"W Zhang","year":"2023","unstructured":"Zhang, W. et al. Enhancing stability in cardiovascular disease risk prediction: A deep learning approach leveraging retinal images. Inform. Med. Unlocked 42, 101366 (2023).","journal-title":"Inform. Med. Unlocked"},{"key":"1436_CR38","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1161\/01.CIR.99.9.1165","volume":"99","author":"P Jousilahti","year":"1999","unstructured":"Jousilahti, P., Vartiainen, E., Tuomilehto, J. & Puska, P. Sex, age, cardiovascular risk factors, and coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation 99, 1165\u20131172 (1999).","journal-title":"Circulation"},{"key":"1436_CR39","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1016\/S0140-6736(08)60313-X","volume":"371","author":"AD Sniderman","year":"2008","unstructured":"Sniderman, A. D. & Furberg, C. D. Age as a modifiable risk factor for cardiovascular disease. Lancet 371, 1547\u20131549 (2008).","journal-title":"Lancet"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01436-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01436-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01436-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T05:56:09Z","timestamp":1740376569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01436-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1436"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01436-1","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"122"}}