Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes

Low Wang CC, Hess CN, Hiatt WR, Goldfine AB. Clinical update: cardiovascular disease in diabetes mellitus: atherosclerotic cardiovascular disease and heart failure in type 2 diabetes mellitus—mechanisms, management, and clinical considerations. Circulation. 2016;133:2459–502.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Booth GL, Kapral MK, Fung K, Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet. 2006;368:29–36.

Article  PubMed  Google Scholar 

American Diabetes Association Professional Practice C. 10. Cardiovascular disease and risk management: standards of medical care in diabetes-2022. Diabetes Care. 2022;45:S144-S174.

McGuire DK, Shih WJ, Cosentino F, Charbonnel B, Cherney DZI, Dagogo-Jack S, Pratley R, Greenberg M, Wang S, Huyck S, Gantz I, Terra SG, Masiukiewicz U, Cannon CP. Association of SGLT2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes: a meta-analysis. JAMA Cardiol. 2021;6:148–58.

Article  PubMed  Google Scholar 

Sattar N, Lee MMY, Kristensen SL, Branch KRH, Del Prato S, Khurmi NS, Lam CSP, Lopes RD, McMurray JJV, Pratley RE, Rosenstock J, Gerstein HC. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of randomised trials. Lancet Diabetes Endocrinol. 2021;9:653–62.

Article  CAS  PubMed  Google Scholar 

Amoaku WM, Ghanchi F, Bailey C, Banerjee S, Banerjee S, Downey L, Gale R, Hamilton R, Khunti K, Posner E, Quhill F, Robinson S, Setty R, Sim D, Varma D, Mehta H. Diabetic retinopathy and diabetic macular oedema pathways and management: UK consensus working group. Eye. 2020;34:1–51.

Article  PubMed  PubMed Central  Google Scholar 

Cheung N, Wang JJ, Klein R, Couper DJ, Sharrett AR, Wong TY. Diabetic retinopathy and the risk of coronary heart disease: the atherosclerosis risk in communities study. Diabetes Care. 2007;30:1742–6.

Article  PubMed  Google Scholar 

Cheung N, Wang JJ, Rogers SL, Brancati F, Klein R, Sharrett AR, Wong TY, Investigators AS. Diabetic retinopathy and risk of heart failure. J Am Coll Cardiol. 2008;51:1573–8.

Article  PubMed  Google Scholar 

Wong KH, Hu K, Peterson C, Sheibani N, Tsivgoulis G, Majersik JJ, de Havenon AH. Diabetic retinopathy and risk of stroke: a secondary analysis of the ACCORD eye study. Stroke. 2020;51:3733–6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mordi IR, Tee A, Palmer CN, McCrimmon RJ, Doney ASF, Lang CC. Microvascular disease and heart failure with reduced and preserved ejection fraction in type 2 diabetes. ESC Heart Fail. 2020;7:1168–77.

Article  PubMed  PubMed Central  Google Scholar 

Mordi IR, Trucco E, Syed MG, MacGillivray T, Nar A, Huang Y, George G, Hogg S, Radha V, Prathiba V, Anjana RM, Mohan V, Palmer CNA, Pearson ER, Lang CC, Doney ASF. Prediction of major adverse cardiovascular events from retinal, clinical, and genomic data in individuals with type 2 diabetes: a population cohort study. Diabetes Care. 2022;45:710–6.

Article  CAS  PubMed  Google Scholar 

Liew G, Mitchell P, Rochtchina E, Wong TY, Hsu W, Lee ML, Wainwright A, Wang JJ. Fractal analysis of retinal microvasculature and coronary heart disease mortality. Eur Heart J. 2011;32:422–9.

Article  PubMed  Google Scholar 

Sandoval-Garcia E, McLachlan S, Price AH, MacGillivray TJ, Strachan MWJ, Wilson JF, Price JF. Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia. 2021;64:2215–27.

Article  PubMed  PubMed Central  Google Scholar 

McGeechan K, Liew G, Macaskill P, Irwig L, Klein R, Klein BE, Wang JJ, Mitchell P, Vingerling JR, Dejong PT, Witteman JC, Breteler MM, Shaw J, Zimmet P, Wong TY. Meta-analysis: retinal vessel caliber and risk for coronary heart disease. Ann Intern Med. 2009;151:404–13.

Article  PubMed  PubMed Central  Google Scholar 

Zekavat SM, Raghu VK, Trinder M, Ye Y, Koyama S, Honigberg MC, Yu Z, Pampana A, Urbut S, Haidermota S, O’Regan DP, Zhao H, Ellinor PT, Segre AV, Elze T, Wiggs JL, Martone J, Adelman RA, Zebardast N, Del Priore L, Wang JC, Natarajan P. Deep learning of the retina enables phenome-and genome-wide analyses of the microvasculature. Circulation. 2022;145:134–50.

Article  PubMed  Google Scholar 

Arnould L, Binquet C, Guenancia C, Alassane S, Kawasaki R, Daien V, Tzourio C, Kawasaki Y, Bourredjem A, Bron A, Creuzot-Garcher C. Association between the retinal vascular network with Singapore I Vessel Assessment (SIVA) software, cardiovascular history and risk factors in the elderly: the Montrachet study, population-based study. PLoS ONE. 2018;13:e0194694.

Article  PubMed  PubMed Central  Google Scholar 

Wagner SK, Fu DJ, Faes L, Liu X, Huemer J, Khalid H, Ferraz D, Korot E, Kelly C, Balaskas K, Denniston AK, Keane PA. Insights into systemic disease through retinal imaging-based oculomics. Transl Vis Sci Technol. 2020;9:6.

Article  PubMed  PubMed Central  Google Scholar 

Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2:158–64.

Article  PubMed  Google Scholar 

Rim TH, Lee CJ, Tham YC, Cheung N, Yu M, Lee G, Kim Y, Ting DSW, Chong CCY, Choi YS, Yoo TK, Ryu IH, Baik SJ, Kim YA, Kim SK, Lee SH, Lee BK, Kang SM, Wong EYM, Kim HC, Kim SS, Park S, Cheng CY, Wong TY. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021;3:e306–16.

Article  CAS  PubMed  Google Scholar 

Chang J, Ko A, Park SM, Choi S, Kim K, Kim SM, Yun JM, Kang U, Shin IH, Shin JY, Ko T, Lee J, Oh BL, Park KH. Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images. Am J Ophthalmol. 2020;217:121–30.

Article  PubMed  Google Scholar 

Arnould L, Guenancia C, Bourredjem A, Binquet C, Gabrielle PH, Eid P, Baudin F, Kawasaki R, Cottin Y, Creuzot-Garcher C, Jacquir S. Prediction of cardiovascular parameters with supervised machine learning from Singapore I vessel assessment and OCT-angiography: a pilot study. Transl Vis Sci Technol. 2021;10:20.

Article  PubMed  PubMed Central  Google Scholar 

Hébert HL, Shepherd B, Milburn K, Veluchamy A, Meng W, Carr F, Donnelly LA, Tavendale R, Leese G, Colhoun HM, Dow E, Morris AD, Doney AS, Lang CC, Pearson ER, Smith BH, Palmer CNA. Cohort profile: genetics of diabetes audit and research in Tayside Scotland (GoDARTS). Int J Epidemiol. 2018;47:380–j381.

Article  PubMed  Google Scholar 

Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, Michos ED, Miedema MD, Munoz D, Smith SC Jr., Virani SS, Williams KA, Sr., Yeboah J, Ziaeian B. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. Circulation. 2019;140:e596–646.

PubMed  PubMed Central  Google Scholar 

Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Syed MG, Doney A, George G, Mordi I, Trucco E. Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort. 2021:109–18.

Syed MG. Investigating the retina as a source of biomarkers for systemic conditions using artificial intelligence. 2023.

Tan M, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks. Paper presented at: proceedings of the 36th international conference on machine learning; 2019; Proceedings of machine learning research.

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52.

Article  Google Scholar 

Yu Y, Lin H, Meng J, Wei X, Guo H, Zhao Z. Deep transfer learning for modality classification of medical images. Information. 2017;8:91.

Article  Google Scholar 

HIC. Health Informatics Centre Services. 2021, June 2.

Bradski G, Kaehler A, OpenCV. Dr Dobb’s J Softw Tools. 2000;3.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar 

Chollet F. keras. 2015.

Qubvel EN. 2021, June 21.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision. 2017:618–626.

Gerrits N, Elen B, Van Craenendonck T, Triantafyllidou D, Petropoulos IN, Malik RA, De Boever P. Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images. Sci Rep. 2020;10:1–9.

Article  Google Scholar 

Rim TH, Lee G, Kim Y, Tham Y-C, Lee CJ, Baik SJ, Kim YA, Yu M, Deshmukh M, Lee BK. Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. Lancet Digit Health. 2020;2:e526–36.

Article  PubMed  Google Scholar 

Kim YD, Noh KJ, Byun SJ, Lee S, Kim T, Sunwoo L, Lee KJ, Kang S-H, Park KH, Park SJ. Effects of hypertension, diabetes, and smoking on age and sex prediction from retinal fundus images. Sci Rep. 2020;10:1–14.

留言 (0)

沒有登入
gif