Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders

Yimin Qu, Jack Jock-Wai Lee, Yuanyuan Zhuo, Shukai Liu, Rebecca L Thomas, David R Owens, Benny Chung-Ying Zee, Yimin Qu, Jack Jock-Wai Lee, Yuanyuan Zhuo, Shukai Liu, Rebecca L Thomas, David R Owens, Benny Chung-Ying Zee

Abstract

Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders.

Methods: We have conducted a case-control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results.

Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes.

Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Keywords: cardiometabolic disorders; coronary heart disease; machine learning; retinal images.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
The ROC curve of the classification model for CHD.
Figure 2
Figure 2
The classification model for CHD in box plot.
Figure 3
Figure 3
Subgroup analysis with classification models for CHD patients (A) without and (B) without diabetes in box plot.

References

    1. Wang H., Naghavi M., Allen C., Barber R.M., Bhutta Z.A., Carter A., Casey D.C., Charlson F.J., Chen A.Z., Coates M.M. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459–1544. doi: 10.1016/S0140-6736(16)31012-1.
    1. Sanchis-Gomar F., Perez-Quilis C., Leischik R., Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Ann. Transl. Med. 2016;4:256. doi: 10.21037/atm.2016.06.33.
    1. Guilbert J.J. The world health report 2002—Reducing risks, promoting healthy life. Educ. Health. 2003;16:230. doi: 10.1080/1357628031000116808.
    1. Bhatnagar P., Wickramasinghe K., Wilkins E., Townsend N. Trends in the epidemiology of cardiovascular disease in the UK. Heart. 2016;102:1945–1952. doi: 10.1136/heartjnl-2016-309573.
    1. Dalen J.E., Alpert J.S., Goldberg R.J., Weinstein R.S. The epidemic of the 20(th) century: Coronary heart disease. Am. J. Med. 2014;127:807–812. doi: 10.1016/j.amjmed.2014.04.015.
    1. Gupta R., Mohan I., Narula J. Trends in Coronary Heart Disease Epidemiology in India. Ann. Glob. Health. 2016;82:307–315. doi: 10.1016/j.aogh.2016.04.002.
    1. Zhu K.F., Wang Y.M., Zhu J.Z., Zhou Q.Y., Wang N.F. National prevalence of coronary heart disease and its relationship with human development index: A systematic review. Eur. J. Prev. Cardiol. 2016;23:530–543. doi: 10.1177/2047487315587402.
    1. Centers for Disease Control and Prevention Prevalence of coronary heart disease—United States, 2006–2010. MMWR Morb. Mortal. Wkly. Rep. 2011;60:1377–1381.
    1. Ferreira-González I. The Epidemiology of Coronary Heart Disease. Rev. Española Cardiol. Engl. Ed. 2014;67:139–144. doi: 10.1016/j.recesp.2013.10.003.
    1. Gaziano T.A., Bitton A., Anand S., Abrahams-Gessel S., Murphy A. Growing epidemic of coronary heart disease in low- and middle-income countries. Curr. Probl. Cardiol. 2010;35:72–115. doi: 10.1016/j.cpcardiol.2009.10.002.
    1. Zhang G., Yu C., Zhou M., Wang L., Zhang Y., Luo L. Burden of Ischaemic heart disease and attributable risk factors in China from 1990 to 2015: Findings from the global burden of disease 2015 study. BMC Cardiovasc. Disord. 2018;18:18. doi: 10.1186/s12872-018-0761-0.
    1. Wang Y., Li Y., Liu X., Zhang H., Abdulai T., Tu R., Tian Z., Qian X., Jiang J., Qiao D., et al. Prevalence and Influencing Factors of Coronary Heart Disease and Stroke in Chinese Rural Adults: The Henan Rural Cohort Study. Front. Public Health. 2020;7:411. doi: 10.3389/fpubh.2019.00411.
    1. Parish S., Arnold M., Clarke R., Du H., Wan E., Kurmi O., Chen Y., Guo Y., Bian Z., Collins R., et al. Assessment of the Role of Carotid Atherosclerosis in the Association Between Major Cardiovascular Risk Factors and Ischemic Stroke Subtypes. JAMA. 2019;2:e194873. doi: 10.1001/jamanetworkopen.2019.4873.
    1. National Health and Family Planning Commission . China Health and Family Planning Statistical Yearbook 2017. Peking Union Medical College; Beijing, China: 2017.
    1. Moran A., Zhao D., Gu D., Coxson P., Chen C.-S., Cheng J., Liu J., He J., Goldman L. The future impact of population growth and aging on coronary heart disease in China: Projections from the Coronary Heart Disease Policy Model-China. BMC Public Health. 2008;8:394. doi: 10.1186/1471-2458-8-394.
    1. Lown B., Wolf M. Approaches to sudden death from coronary heart disease. Circulation. 1971;44:130–142. doi: 10.1161/01.CIR.44.1.130.
    1. Yang B.Y., Hu L.W., Jalaludin B., Knibbs L.D., Markevych I., Heinrich J., Bloom M.S., Morawska L., Lin S., Jalava P., et al. Association Between Residential Greenness, Cardiometabolic Disorders, and Cardiovascular Disease Among Adults in China. JAMA. 2020;3:e2017507. doi: 10.1001/jamanetworkopen.2020.17507.
    1. Forouzanfar M.H., Alexander L., Anderson H.R., Bachman V.F., Biryukov S., Brauer M., Burnett R., Casey D., Coates M.M., Cohen A., et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:2287–2323. doi: 10.1016/S0140-6736(15)00128-2.
    1. Gerdts E., Regitz-Zagrosek V. Sex differences in cardiometabolic disorders. Nat. Med. 2019;25:1657–1666. doi: 10.1038/s41591-019-0643-8.
    1. Kokubo Y., Matsumoto C. Hypertension Is a Risk Factor for Several Types of Heart Disease: Review of Prospective Studies. Adv. Exp. Med. Biol. 2017;956:419–426. doi: 10.1007/5584_2016_99.
    1. Xu G., You D., Wong L., Duan D., Kong F., Zhang X., Zhao J., Xing W., Han L., Li L. Risk of all-cause and CHD mortality in women versus men with type 2 diabetes: A systematic review and meta-analysis. Eur. J. Endocrinol. 2019;180:243–255. doi: 10.1530/EJE-18-0792.
    1. Reaven G.M. Multiple CHD risk factors in type 2 diabetes: Beyond hyperglycaemia. Diabetes Obes. Metab. 2002;4((Suppl. 1)):S13–S18. doi: 10.1046/j.1462-8902.2001.00037.x.
    1. Temple N.J. Fat, Sugar, Whole Grains and Heart Disease: 50 Years of Confusion. Nutrients. 2018;10:39. doi: 10.3390/nu10010039.
    1. DiNicolantonio J.J., Lucan S.C., O’Keefe J.H. The Evidence for Saturated Fat and for Sugar Related to Coronary Heart Disease. Prog. Cardiovasc. Dis. 2016;58:464–472. doi: 10.1016/j.pcad.2015.11.006.
    1. Tziomalos K., Athyros V.G., Karagiannis A., Mikhailidis D.P. Dyslipidemia as a risk factor for ischemic stroke. Curr. Top. Med. Chem. 2009;9:1291–1297. doi: 10.2174/156802609789869628.
    1. Yu J.N., Cunningham J.A., Thouin S.R., Gurvich T., Liu D. Hyperlipidemia. Prim. Care. 2000;27:541–587. doi: 10.1016/S0095-4543(05)70164-0.
    1. Zhang X.-F., Attia J., D’Este C., Yu X.-H., Wu X.-G. A risk score predicted coronary heart disease and stroke in a Chinese cohort. J. Clin. Epidemiol. 2005;58:951–958. doi: 10.1016/j.jclinepi.2005.01.013.
    1. Wilson P.W.F., D’Agostino R.B., Levy D., Belanger A.M., Silbershatz H., Kannel W.B. Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation. 1998;97:1837–1847. doi: 10.1161/01.CIR.97.18.1837.
    1. Anderson K.M., Wilson P.W., Odell P.M., Kannel W.B. An updated coronary risk profile. A statement for health professionals. Circulation. 1991;83:356–362. doi: 10.1161/01.CIR.83.1.356.
    1. Anderson K.M., Odell P.M., Wilson P.W., Kannel W.B. Cardiovascular disease risk profiles. Am. Heart J. 1991;121:293–298. doi: 10.1016/0002-8703(91)90861-B.
    1. Liu J., Hong Y., D’Agostino R.B., Sr., Wu Z., Wang W., Sun J., Wilson P.W., Kannel W.B., Zhao D. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA. 2004;291:2591–2599. doi: 10.1001/jama.291.21.2591.
    1. Chambless L.E., Folsom A.R., Sharrett A.R., Sorlie P., Couper D., Szklo M., Nieto F.J. Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. J. Clin. Epidemiol. 2003;56:880–890. doi: 10.1016/S0895-4356(03)00055-6.
    1. Assmann G., Cullen P., Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study. Circulation. 2002;105:310–315. doi: 10.1161/hc0302.102575.
    1. Ferrario M., Chiodini P., Chambless L.E., Cesana G., Vanuzzo D., Panico S., Sega R., Pilotto L., Palmieri L., Giampaoli S. Prediction of coronary events in a low incidence population. Assessing accuracy of the CUORE Cohort Study prediction equation. Int. J. Epidemiol. 2005;34:413–421. doi: 10.1093/ije/dyh405.
    1. Stevens R.J., Kothari V., Adler A.I., Stratton I.M. The UKPDS risk engine: A model for the risk of coronary heart disease in Type II diabetes (UKPDS 56) Clin. Sci. 2001;101:671–679. doi: 10.1042/CS20000335.
    1. Pekkanen J., Tervahauta M., Nissinen A., Karvonen M.J. Does the predictive value of baseline coronary risk factors change over a 30-year follow-up? Cardiology. 1993;82:181–190. doi: 10.1159/000175867.
    1. Benfante R., Reed D. Is elevated serum cholesterol level a risk factor for coronary heart disease in the elderly? JAMA. 1990;263:393–396. doi: 10.1001/jama.1990.03440030080025.
    1. Menotti A., Blackburn H., Kromhout D., Nissinen A., Karvonen M., Aravanis C., Dontas A., Fidanza F., Giampaoli S. The inverse relation of average population blood pressure and stroke mortality rates in the seven countries study: A paradox. Eur. J. Epidemiol. 1997;13:379–386. doi: 10.1023/A:1007326624702.
    1. Wong T.Y., Klein R., Klein B.E.K., Tielsch J.M., Hubbard L., Nieto F.J. Retinal Microvascular Abnormalities and their Relationship with Hypertension, Cardiovascular Disease, and Mortality. Surv. Ophthalmol. 2001;46:59–80. doi: 10.1016/S0039-6257(01)00234-X.
    1. Wu Y., Liu X., Li X., Li Y., Zhao L., Chen Z., Li Y., Rao X., Zhou B., Detrano R., et al. Estimation of 10-year risk of fatal and nonfatal ischemic cardiovascular diseases in Chinese adults. Circulation. 2006;114:2217–2225. doi: 10.1161/CIRCULATIONAHA.105.607499.
    1. Patton N., Aslam T., Macgillivray T., Pattie A., Deary I.J., Dhillon B. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: A rationale based on homology between cerebral and retinal microvasculatures. J. Anat. 2005;206:319–348. doi: 10.1111/j.1469-7580.2005.00395.x.
    1. Cheng L., Barlis P., Gibson J., Colville D., Hutchinson A., Gleeson G., Lamoureux E., VanGaal W., Savige J. Microvascular retinopathy and angiographically-demonstrated coronary artery disease: A cross-sectional, observational study. PLoS ONE. 2018;13:e0192350. doi: 10.1371/journal.pone.0192350.
    1. Tabatabaee A., Asharin M.R., Dehghan M.H., Pourbehi M.R., Nasiri-Ahmadabadi M., Assadi M. Retinal vessel abnormalities predict coronary artery diseases. Perfusion. 2013;28:232–237. doi: 10.1177/0267659112473173.
    1. Moss H.E. Retinal Vascular Changes are a Marker for Cerebral Vascular Diseases. Curr. Neurol. Neurosci. Rep. 2015;15:40. doi: 10.1007/s11910-015-0561-1.
    1. Zhuo Y., Yu H., Yang Z., Zee B., Lee J., Kuang L. Prediction Factors of Recurrent Stroke among Chinese Adults Using Retinal Vasculature Characteristics. J. Stroke Cerebrovasc. Dis. 2017;26:679–685. doi: 10.1016/j.jstrokecerebrovasdis.2017.01.020.
    1. Wang J., Leng F., Li Z., Tang X., Qian H., Li X., Zhang Y., Chen X., Du H., Liu P. Retinal vascular abnormalities and their associations with cardiovascular and cerebrovascular diseases: A Study in rural southwestern Harbin, China. BMC Ophthalmol. 2020;20:136. doi: 10.1186/s12886-020-01407-y.
    1. Juutilainen A., Lehto S., Rönnemaa T., Pyörälä K., Laakso M. Retinopathy predicts cardiovascular mortality in type 2 diabetic men and women. Diabetes Care. 2007;30:292–299. doi: 10.2337/dc06-1747.
    1. Wang J.J., Liew G., Wong T.Y., Smith W., Klein R., Leeder S.R., Mitchell P. Retinal vascular calibre and the risk of coronary heart disease-related death. Heart Br. Card. Soc. 2006;92:1583–1587. doi: 10.1136/hrt.2006.090522.
    1. Klein R., Klein B.E., Moss S.E., Wong T.Y. Retinal vessel caliber and microvascular and macrovascular disease in type 2 diabetes: XXI: The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmology. 2007;114:1884–1892. doi: 10.1016/j.ophtha.2007.02.023.
    1. Doubal F.N., de Haan R., MacGillivray T.J., Cohn-Hokke P.E., Dhillon B., Dennis M.S., Wardlaw J.M. Retinal arteriolar geometry is associated with cerebral white matter hyperintensities on magnetic resonance imaging. Int. J. Stroke Off. J. Int. Stroke Soc. 2010;5:434–439. doi: 10.1111/j.1747-4949.2010.00483.x.
    1. Witt N., Wong T.Y., Hughes A.D., Chaturvedi N., Klein B.E., Evans R., McNamara M., Thom S.A., Klein R. Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension. 2006;47:975–981. doi: 10.1161/01.HYP.0000216717.72048.6c.
    1. Fihn S.D., Blankenship J.C., Alexander K.P., Bittl J.A., Byrne J.G., Fletcher B.J., Fonarow G.C., Lange R.A., Levine G.N., Maddox T.M., et al. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J. Thorac. Cardiovasc. Surg. 2015;149:e5–e23. doi: 10.1016/j.jtcvs.2014.11.002.
    1. Amsterdam E.A., Wenger N.K., Brindis R.G., Casey D.E., Ganiats T.G., Holmes D.R., Jaffe A.S., Jneid H., Kelly R.F., Kontos M.C., et al. 2014 AHA/ACC Guideline for the Management of Patients with Non–ST-Elevation Acute Coronary Syndromes: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 2014;64:e139–e228. doi: 10.1016/j.jacc.2014.09.017.
    1. Ibanez B., James S., Agewall S., Antunes M.J., Bucciarelli-Ducci C., Bueno H., Caforio A.L.P., Crea F., Goudevenos J.A., Halvorsen S., et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC) Eur. Heart J. 2018;39:119–177. doi: 10.1093/eurheartj/ehx393.
    1. Zee B.C.-y., Lee J.J.-w., Li E.Q. Method and Device for Retinal Image Analysis. 8,787,638. U.S. Patent. 2014 July 22;
    1. Lai M., Lee J., Chiu S., Charm J., So W.Y., Yuen F.P., Kwok C., Tsoi J., Lin Y., Zee B. A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. eClinicalMedicine. 2020;28:100588. doi: 10.1016/j.eclinm.2020.100588.
    1. Guo V.Y., Cao B., Wu X., Lee J.J.W., Zee B.C. Prospective Association between Diabetic Retinopathy and Cardiovascular Disease-A Systematic Review and Meta-analysis of Cohort Studies. J. Stroke Cerebrovasc. Dis. 2016;25:1688–1695. doi: 10.1016/j.jstrokecerebrovasdis.2016.03.009.
    1. Guo V.Y., Chan J.C., Chung H., Ozaki R., So W., Luk A., Lam A., Lee J., Zee B.C. Retinal Information is Independently Associated with Cardiovascular Disease in Patients with Type 2 diabetes. Sci. Rep. 2016;6:19053. doi: 10.1038/srep19053.
    1. Fan R.-E., Chen P.-H., Lin C.-J., Joachims T. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 2005;6:1889–1918.
    1. Schölkopf B., Smola A.J., Bach F. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press; Cambridge, MA, USA: 2002.
    1. Newcombe R.G. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Stat. Med. 1998;17:857–872. doi: 10.1002/(SICI)1097-0258(19980430)17:8<857::AID-SIM777>;2-E.
    1. Lau A.Y., Mok V., Lee J., Fan Y., Zeng J., Lam B., Wong A., Kwok C., Lai M., Zee B. Retinal image analytics detects white matter hyperintensities in healthy adults. Ann. Clin. Transl. Neurol. 2019;6:98–105. doi: 10.1002/acn3.688.
    1. Tedeschi-Reiner E., Strozzi M., Skoric B., Reiner Z. Relation of Atherosclerotic Changes in Retinal Arteries to the Extent of Coronary Artery Disease. Am. J. Cardiol. 2005;96:1107–1109. doi: 10.1016/j.amjcard.2005.05.070.
    1. Tedeschi-Reiner E., Reiner Z., Sonicki Z. Atherosclerosis of retinal arteries in men: Role of serum lipoproteins and apoproteins. Croat. Med. J. 2004;45:333–337.
    1. Theuerle J.D., Al-Fiadh A.H., Amirul Islam F.M., Patel S.K., Burrell L.M., Wong T.Y., Farouque O. Impaired retinal microvascular function predicts long-term adverse events in patients with cardiovascular disease. Cardiovasc. Res. 2021;117:1949–1957. doi: 10.1093/cvr/cvaa245.
    1. McGeechan K., Liew G., Macaskill P., Irwig L., Klein R., Klein B.E., Wang J.J., Mitchell P., Vingerling J.R., Dejong P.T., et al. Meta-analysis: Retinal vessel caliber and risk for coronary heart disease. Ann. Intern. Med. 2009;151:404–413. doi: 10.7326/0003-4819-151-6-200909150-00005.
    1. Wang N., Liang C. Relationship of Gensini score with retinal vessel diameter and arteriovenous ratio in senile CHD. Open Life Sci. 2021;16:737–745. doi: 10.1515/biol-2021-0068.
    1. Cordina R., Leaney J., Golzan M., Grieve S., Celermajer D.S., Graham S.L. Ophthalmological consequences of cyanotic congenital heart disease: Vascular parameters and nerve fibre layer. Clin. Exp. Ophthalmol. 2015;43:115–123. doi: 10.1111/ceo.12401.
    1. Hart W.E., Goldbaum M., Côté B., Kube P., Nelson M.R. Measurement and classification of retinal vascular tortuosity. Int. J. Med. Inform. 1999;53:239–252. doi: 10.1016/S1386-5056(98)00163-4.
    1. Vilela M.A., Amaral C.E., Ferreira M.A.T. Retinal vascular tortuosity: Mechanisms and measurements. Eur. J. Ophthalmol. 2021;31:1497–1506. doi: 10.1177/1120672120979907.
    1. Kim B.J., Kim S.M., Kang D.W., Kwon S.U., Suh D.C., Kim J.S. Vascular tortuosity may be related to intracranial artery atherosclerosis. Int. J. Stroke. 2015;10:1081–1086. doi: 10.1111/ijs.12525.
    1. Cheung C.Y., Zheng Y., Hsu W., Lee M.L., Lau Q.P., Mitchell P., Wang J.J., Klein R., Wong T.Y. Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology. 2011;118:812–818. doi: 10.1016/j.ophtha.2010.08.045.
    1. Sasongko M.B., Wong T.Y., Nguyen T.T., Cheung C.Y., Shaw J.E., Wang J.J. Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy. Diabetologia. 2011;54:2409–2416. doi: 10.1007/s00125-011-2200-y.
    1. Sandoval-Garcia E., McLachlan S., Price A.H., MacGillivray T.J., Strachan M.W.J., Wilson J.F., Price J.F. Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia. 2021;64:2215–2227. doi: 10.1007/s00125-021-05499-z.
    1. Duncan B.B., Wong T.Y., Tyroler H.A., Davis C.E., Fuchs F.D. Hypertensive retinopathy and incident coronary heart disease in high risk men. Br. J. Ophthalmol. 2002;86:1002–1006. doi: 10.1136/bjo.86.9.1002.
    1. Hu Y.H., Pan X.R., Liu P.A., Li G.W., Howard B.V., Bennett P.H. Coronary heart disease and diabetic retinopathy in newly diagnosed diabetes in Da Qing, China: The Da Qing IGT and Diabetes Study. Acta Diabetol. 1991;28:169–173. doi: 10.1007/BF00579721.
    1. Martin S.C., Butcher A., Martin N., Farmer J., Dobson P.M., Bartlett W.A., Jones A.F. Cardiovascular risk assessment in patients with retinal vein occlusion. Br. J. Ophthalmol. 2002;86:774–776. doi: 10.1136/bjo.86.7.774.
    1. Rim T.H., Lee C.J., Tham Y.C., Cheung N., Yu M., Lee G., Kim Y., Ting D.S.W., Chong C.C.Y., Choi Y.S., et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit. Health. 2021;3:e306–e316. doi: 10.1016/S2589-7500(21)00043-1.
    1. Ma Y., Xiong J., Zhu Y., Ge Z., Hua R., Fu M., Li C., Wang B., Dong L., Zhao X., et al. Deep learning algorithm using fundus photographs for 10-year risk assessment of ischemic cardiovascular diseases in China. Sci. Bull. 2022;67:17–20. doi: 10.1016/j.scib.2021.08.016.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa