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Erschienen in:

01.07.2019 | Cardiovascular Disease and Stroke (S. Prabhakaran, Section Editor)

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography

verfasst von: Ankush Jamthikar, Deep Gupta, Narendra N. Khanna, Tadashi Araki, Luca Saba, Andrew Nicolaides, Aditya Sharma, Tomaz Omerzu, Harman S. Suri, Ajay Gupta, Sophie Mavrogeni, Monika Turk, John R. Laird, Athanasios Protogerou, Petros P. Sfikakis, George D. Kitas, Vijay Viswanathan, Gyan Pareek, Martin Miner, Jasjit S. Suri

Erschienen in: Current Atherosclerosis Reports | Ausgabe 7/2019

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Abstract

Purpose of Review

Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography.

Recent Findings

In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients’ demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks.

Summary

Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

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Cardiovascular diseases (CVDs): key facts by WHO 2016 [http://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)]. Accessed 1 Oct 2018.

Rosengren A, Hawken S, Ôunpuu S, et al. Association of psychosocial risk factors with risk of acute myocardial infarction in 11 119 cases and 13 648 controls from 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):953–62.PubMed

• O’Donnell MJ, Chin SL, Rangarajan S, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet. 2016;388(10046):761–75 An important logitudinal study that associated the convetional risk factors with risk of stroke events. PubMed

O’Donnell MJ, Xavier D, Liu L, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet. 2010;376(9735):112–23.PubMed

Stevens RJ, Coleman RL, Adler AI, Stratton IM, Matthews DR, Holman RR. Risk factors for myocardial infarction case fatality and stroke case fatality in type 2 diabetes: UKPDS 66. Diabetes Care. 2004;27(1):201–7.PubMed

Saba L, Molinari F, Meiburger K, et al. What is the correct distance measurement metric when measuring carotid ultrasound intima-media thickness automatically? Int Angiol. 2012;31(5):483–9.PubMed

Conroy R, Pyörälä K, Fitzgerald A, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.PubMed

D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.PubMed

Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611–9.PubMed

10.

Stevens RJ, Kothari V, Adler AI, Stratton IM, Holman RR. The UKPDS risk engine: a model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci. 2001;101(6):671–9.PubMed

11.

Kothari V, Stevens RJ, Adler AI, et al. UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke. 2002;33(7):1776–81.PubMed

12.

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. 2017;357:j2099.PubMedPubMedCentral

13.

Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Part B):2935–59.PubMed

14.

Group NDR. Risk assessment chart for death from cardiovascular disease based on a 19-year follow-up study of a Japanese representative population. Circ J. 2006;70(10):1249–55.

15.

Nobel L, Mayo NE, Hanley J, Nadeau L, Daskalopoulou SS. MyRisk_Stroke Calculator: a personalized stroke risk assessment tool for the general population. J Clin Neurol. 2014;10(1):1–9.PubMedPubMedCentral

16.

Bonek K, Głuszko P. Cardiovascular risk assessment in rheumatoid arthritis–controversies and the new approach. Reumatologia. 2016;54(3):128–35.PubMedPubMedCentral

17.

Arts E, Popa C, Den Broeder A, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis. 2014;74(4):668–74 annrheumdis-2013-204024.PubMed

18.

Garg N, Muduli SK, Kapoor A, et al. Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses. Indian Heart J. 2017;69(4):458–63.PubMedPubMedCentral

19.

Mathiesen Ellisiv B, Johnsen Stein H, Wilsgaard T, Bønaa Kaare H, Løchen M-L, Njølstad I. Carotid plaque area and intima-media thickness in prediction of first-ever ischemic stroke. Stroke. 2011;42(4):972–8.PubMed

20.

Spence JD, Eliasziw M, DiCicco M, Hackam DG, Galil R, Lohmann T. Carotid plaque area: a tool for targeting and evaluating vascular preventive therapy. Stroke. 2002;33(12):2916–22.PubMed

21.

Belcaro G, Nicolaides AN, Ramaswami G, et al. Carotid and femoral ultrasound morphology screening and cardiovascular events in low risk subjects: a 10-year follow-up study (the CAFES-CAVE study(1)). Atherosclerosis. 2001;156(2):379–87.PubMed

22.

Garcia-Garcia HM, Costa MA, Serruys PW. Imaging of coronary atherosclerosis: intravascular ultrasound. Eur Heart J. 2010;31(20):2456–69.PubMed

23.

Banchhor SK, Araki T, Londhe ND, et al. Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: a comparative approach. Comput Methods Prog Biomed. 2016;134:237–58.

24.

Van Soest G, Regar E, KoljenoviÄ S, et al. Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging. J Biomed Opt. 2010;15(1):011105–9.PubMed

25.

Boi A, Jamthikar AD, Saba L, et al. A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. Curr Atheroscler Rep. 2018;20(7):33.PubMed

26.

Blaha MJ, Mortensen MB, Kianoush S, Tota-Maharaj R, Cainzos-Achirica M. Coronary artery calcium scoring: is it time for a change in methodology? JACC Cardiovasc Imaging. 2017;10(8):923–37.PubMed

27.

Eckert J, Schmidt M, Magedanz A, Voigtländer T, Schmermund A. Coronary CT angiography in managing atherosclerosis. Int J Mol Sci. 2015;16(2):3740–56.PubMedPubMedCentral

28.

Nambi V, Chambless L, Folsom AR, et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol. 2010;55(15):1600–7.PubMedPubMedCentral

29.

Naqvi TZ, Lee M-S. Carotid intima-media thickness and plaque in cardiovascular risk assessment. JACC Cardiovasc Imaging. 2014;7(10):1025–38.PubMed

30.

Saba L, Mallarini G, Sanfilippo R, Zeng G, Montisci R, Suri J. Intima media thickness variability (IMTV) and its association with cerebrovascular events: a novel marker of carotid therosclerosis? Cardiovasc Diagn Ther. 2012;2(1):10–8.PubMedPubMedCentral

31.

Cuadrado-Godia E, Maniruzzaman M, Araki T, et al. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort. Comput Biol Med. 2018;101:128–45.PubMed

32.

Laine A, Sanches JM, Suri JS: Ultrasound imaging: advances and applications. Springer; 2012.

33.

Chambless LE, Heiss G, Folsom AR, et al. Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987–1993. Am J Epidemiol. 1997;146(6):483–94.PubMed

34.

O’leary DH, Polak JF, Kronmal RA, et al. Distribution and correlates of sonographically detected carotid artery disease in the Cardiovascular Health Study. The CHS Collaborative Research Group. Stroke. 1992;23(12):1752–60.PubMed

35.

Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. Circulation. 1997;96(5):1432–7.PubMed

36.

Rosvall M, Janzon L, Berglund G, Engström G, Hedblad B. Incident coronary events and case fatality in relation to common carotid intima-media thickness. J Intern Med. 2005;257(5):430–7.PubMed

37.

Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? Ten-year results from the Carotid Atherosclerosis Progression Study (CAPS). Eur Heart J. 2010;31(16):2041–8.PubMed

38.

Khanna NN, Jamthikar AD, Gupta D, et al. Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning-based tissue characterization. Curr Atheroscler Rep. 2019;21(2):7.PubMed

39.

Salonen JT, Salonen R. Ultrasonographically assessed carotid morphology and the risk of coronary heart disease. Arterioscler Thromb Vasc Biol. 1991;11(5):1245–9.

40.

Hirata T, Arai Y, Takayama M, Abe Y, Ohkuma K, Takebayashi T. Carotid plaque score and risk of cardiovascular mortality in the oldest old: results from the TOOTH study. J Atheroscler Thromb. 2018;25(1):55–64.PubMedPubMedCentral

41.

Park HW, Kim WH, Kim KH, et al. Carotid plaque is associated with increased cardiac mortality in patients with coronary artery disease. Int J Cardiol. 2013;166(3):658–63.PubMed

42.

Stein JH, Korcarz CE, Hurst RT, et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr. 2008;21(2):93–111.PubMed

43.

Stein JH, Johnson HM. Carotid intima-media thickness, plaques, and cardiovascular disease risk: implications for preventive cardiology guidelines. In. J Am Coll Cardiol. 2010;55:1608–10.PubMed

44.

Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB Sr. Carotid-wall intima-media thickness and cardiovascular events. N Engl J Med. 2011;365(3):213–21.PubMedPubMedCentral

45.

Allan GM, Garrison S, McCormack J. Comparison of cardiovascular disease risk calculators. Curr Opin Lipidol. 2014;25(4):254–65.PubMed

46.

Conroy RM, on behalf of the Spg, Pyörälä K, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.PubMed

47.

Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2016;38(23):1805–14.PubMedCentral

48.

Biswas M, Kuppili V, Araki T, et al. Deep learning strategy for accurate carotid intima-media thickness measurement: an ultrasound study on Japanese diabetic cohort. Comput Biol Med. 2018;98:100–17.PubMed

49.

Biswas M, Kuppili V, Edla DR, et al. Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Prog Biomed. 2017;155:165–77.

50.

Lam C, Yi D, Guo M, Lindsey T. Automated detection of diabetic retinopathy using deep learning. AMIA Jt Summits Transl Sci Proc. 2017;2018:147–55.

51.

Heo J, Yoon J, Park HJ, Kim YD, Nam HS, Heo JH: Machine learning-based model can predict stroke outcome. In: Am Heart Assoc 2018.

52.

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15.PubMedPubMedCentral

53.

Ambale-Venkatesh B, Wu CO, Liu K, et al. Cardiovascular event prediction by machine learning: the Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121(9):1092–101 CIRCRESAHA. 117.311312.PubMedPubMedCentral

54.

•• Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944 This study compared the ML-based risk startification with convetional risk calculators. PubMedPubMedCentral

55.

Acharya RU, Faust O, Alvin APC, et al. Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst. 2012;36(3):1861–71.PubMed

56.

Acharya UR, Faust O, Alvin A, et al. Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Prog Biomed. 2013;110(1):66–75.

57.

• Acharya UR, Faust O, Sree SV, et al. An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas. 2012;61(4):1045–53 This was an important study that perfromed the carotid atherosclerotic plaque characterization using ML approach.

58.

Acharya UR, Krishnan MMR, Sree SV, et al. Plaque tissue characterization and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation. IEEE Trans Instrum Meas. 2013;62(2):392–400.

59.

Acharya UR, Mookiah MRK, Sree SV, et al. Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput. 2013;51(5):513–23.PubMed

60.

Acharya UR, Sree SV, Krishnan MMR, et al. Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol. 2012;38(6):899–915.PubMed

61.

•• Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7(22):e009476 This is the first of its kind study which have compared the machine learning-based risk calculator with ACC/AHA risk calculator. This article is very important to usage of ML in CVD risk assessment. PubMedPubMedCentral

62.

Ramachandran A, Snehalatha C. Current scenario of diabetes in India. J Diab. 2009;1(1):18–28.

63.

Gupta R, Rao RS, Misra A, Sharma SK. Recent trends in epidemiology of dyslipidemias in India. Indian Heart J. 2017;69(3):382–92.PubMedPubMedCentral

64.

Anchala R, Kannuri NK, Pant H, et al. Hypertension in India: a systematic review and meta-analysis of prevalence, awareness, and control of hypertension. J Hypertens. 2014;32(6):1170–7.PubMedPubMedCentral

65.

van der Meer IM, Iglesias del Sol A, Hak AE, Bots ML, Hofman A, Witteman JC. Risk factors for progression of atherosclerosis measured at multiple sites in the arterial tree: the Rotterdam Study. Stroke. 2003;34(10):2374–9.PubMed

66.

Øygarden H. Carotid intima-media thickness and prediction of cardiovascular disease. J Am Heart Assoc. 2017;6(1):e005313.PubMedPubMedCentral

67.

Khanna NN, Jamthikar AD, Gupta D, et al. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Comput Biol Med. 2019;105:125–43.PubMed

68.

Araki T, Jain PK, Suri HS, et al. Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology: a machine learning paradigm. Comput Biol Med. 2017;80:77–96.PubMed

69.

Bishop CM: Pattern recognition and machine learning. Springer 2006.

70.

Sutton RS, Barto AG: Reinforcement learning: an introduction: MIT press; 2018.

71.

Acharya UR, Sree SV, Krishnan MMR, et al. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images. Comput Methods Prog Biomed. 2013;112(3):624–32.

72.

Shrivastava VK, Londhe ND, Sonawane RS, Suri JS. A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Methods Prog Biomed. 2017;150:9–22.

73.

Banchhor SK, Londhe ND, Araki T, et al. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med. 2017;91:198–212.PubMed

74.

Acharya U, Sree SV, Mookiah M, et al. Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: a pilot study. Proc Inst Mech Eng H J Eng Med. 2013;227(6):643–54.

75.

Than JCM, Saba L, Noor NM, et al. Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework. Comput Biol Med. 2017;89:197–211.PubMed

76.

Shrivastava VK, Londhe ND, Sonawane RS, Suri JS. Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Comput Methods Prog Biomed. 2016;126:98–109.

77.

Pareek G, Acharya UR, Sree SV, et al. Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images. Technol Cancer Res Treat. 2013;12(6):545–57.PubMed

78.

Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan systems. Ultrasonics. 2012;52(4):508–20.PubMed

79.

Molinari F, Meiburger KM, Saba L, et al: Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge® software. In: Multi-modality atherosclerosis imaging and diagnosis. Springer; 2014: 209–219.

80.

Molinari F, Zeng G, Suri JS. Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Trans Ultrason Ferroelectr Freq Control. 2010;57(5):1112–24.PubMed

81.

Molinari F, Meiburger KM, Suri J: Automated high-performance cIMT measurement techniques using patented AtheroEdge™: a screening and home monitoring system. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE: 2011: IEEE; 2011: 6651–6654.

82.

Saba L, Banchhor SK, Araki T, et al. Intra-and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement. Indian Heart J. 2018;70:649–64.PubMed

83.

Stein JH, Tattersall MC. Carotid intima-media thickness and cardiovascular disease risk prediction. J Am Coll Cardiol. 2014;63(21):2301–2.PubMed

84.

Saba L, Banchhor SK, Londhe ND, et al. Web-based accurate measurements of carotid lumen diameter and stenosis severity: an ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials. Comput Biol Med. 2017;91:306–17.PubMed

85.

Krishna Kumar P, Araki T, Rajan J, et al. Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Med Biol Eng Comput. 2017;55(8):1415–34.PubMed

86.

Kumar PK, Araki T, Rajan J, Laird JR, Nicolaides A, Suri JS. State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. Comput Methods Prog Biomed. 2018;163:155–68.

87.

Saba L, Araki T, Kumar PK, et al. Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: an automated tool for stroke analysis. J Clin Ultrasound. 2016;44(4):210–20.PubMed

88.

Muller K-R, Mika S, Ratsch G, Tsuda K, Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw. 2001;12(2):181–201.PubMed

89.

Khanna NN, Jamthikar AD, Araki T, et al. Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: a Japanese diabetes cohort study. Echocardiography. 2019;36:345–61.PubMed

90.

Maniruzzaman M, Rahman MJ, Al-MehediHasan M, et al. Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst. 2018;42(5):92.PubMedPubMedCentral

91.

Maniruzzaman M, Kumar N, Menhazul Abedin M, et al. Comparative approaches for classification of diabetes mellitus data: machine learning paradigm. Comput Methods Prog Biomed. 2017;152:23–34.

92.

Araki T, Ikeda N, Shukla D, et al. A new method for IVUS-based coronary artery disease risk stratification: a link between coronary & carotid ultrasound plaque burdens. Comput Methods Prog Biomed. 2016;124:161–79.

93.

Al’Aref SJ, Anchouche K, Singh G, et al: Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 2018

94.

Chou C-L, Wu Y-J, Hung C-L, et al. Segment-specific prevalence of carotid artery plaque and stenosis in middle-aged adults and elders in Taiwan: a community-based study. J Formos Med Assoc. 2019;118(1):64–71.PubMed

95.

Farkas S, Molnár S, Nagy K, Hortobágyi T, Csiba L. Comparative in vivo and in vitro postmortem ultrasound assessment of intima-media thickness with additional histological analysis in human carotid arteries. Perspect Med. 2012;1(1):170–6.

96.

Gamble G, Beaumont B, Smith H, et al. B-mode ultrasound images of the carotid artery wall: correlation of ultrasound with histological measurements. Atherosclerosis. 1993;102(2):163–73.PubMed

97.

Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

98.

Dalbeni A, Giollo A, Tagetti A, et al. Traditional cardiovascular risk factors or inflammation: which factors accelerate atherosclerosis in arthritis patients? Int J Cardiol. 2017;236:488–92.PubMed

99.

Acharya UR, Sree SV, Molinari F, Saba L, Nicolaides A, Suri JS. An automated technique for carotid far wall classification using grayscale features and wall thickness variability. J Clin Ultrasound. 2015;43(5):302–11.PubMed

100.

Kyriacou EC, Petroudi S, Pattichis CS, et al. Prediction of high-risk asymptomatic carotid plaques based on ultrasonic image features. IEEE Trans Inf Technol Biomed. 2012;16(5):966–73.PubMed

101.

Gastounioti A, Makrodimitris S, Golemati S, Kadoglou NP, Liapis CD, Nikita KS. A novel computerized tool to stratify risk in carotid atherosclerosis using kinematic features of the arterial wall. IEEE J Biomed Health Inform. 2015;19(3):1137–45.PubMed

102.

Hu X, Reaven PD, Saremi A, et al. Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance. EURASIP J Bioinforma Syst Biol. 2016;2016(1):14.

103.

Narain R, Saxena S, Goyal AK. Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Patient Prefer Adherence. 2016;10:1259–70.PubMedPubMedCentral

104.

Unnikrishnan P, Kumar DK, Poosapadi Arjunan S, Kumar H, Mitchell P, Kawasaki R. Development of health parameter model for risk prediction of CVD using SVM. Comput Math Meth Med. 2016;2016:1–7.

105.

Zarkogianni K, Athanasiou M, Thanopoulou AC, Nikita KS. Comparison of machine learning approaches toward assessing the risk of developing cardiovascular disease as a long-term diabetes complication. IEEE J Biomed Health Inform. 2018;22(5):1637–47.PubMed

106.

Saba L, Jain PK, Suri HS, et al. Plaque tissue morphology-based stroke risk stratification using carotid ultrasound: a polling-based PCA learning paradigm. J Med Syst. 2017;41(6):98.PubMed

107.

Motwani M, Dey D, Berman DS, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–7.PubMed

108.

Korshunov VA, Schwartz SM, Berk BC. Vascular remodeling: hemodynamic and biochemical mechanisms underlying Glagov’s phenomenon. Arterioscler Thromb Vasc Biol. 2007;27(8):1722–8.PubMed

109.

Leskinen Y, Lehtimaki T, Loimaala A, et al. Carotid atherosclerosis in chronic renal failure-the central role of increased plaque burden. Atherosclerosis. 2003;171(2):295–302.PubMed

110.

Razzouk L, Rockman CB, Patel MR, et al. Co-existence of vascular disease in different arterial beds: peripheral artery disease and carotid artery stenosis—data from Life Line Screening(®). Atherosclerosis. 2015;241(2):687–91.PubMedPubMedCentral

111.

Banerjee C, Chimowitz MI. Stroke caused by atherosclerosis of the major intracranial arteries. J Vasc Surg. 2017;65(6):1864–5.

112.

Chen PC, Jeng JS, Hsu HC, Su TC, Chien KL, Lee YT. Carotid atherosclerosis progression and risk of cardiovascular events in a community in Taiwan. Sci Rep. 2016;6:25733.PubMedPubMedCentral

113.

Cuadrado-Godia E, Srivastava SK, Saba L, et al: Geometric total plaque area is an equally powerful phenotype compared with carotid intima-media thickness for stroke risk assessment: a deep learning approach. J Vasc Ultrasound 2018. 1544316718806421.

114.

Beach KW: Principles of ultrasonic imaging and instrumentation. In: Ultrasound and carotid bifurcation atherosclerosis. Edited by Nicolaides A, Beach KW, Kyriacou E, Pattichis CS. London: Springer; 2012: 67–96.

115.

Gupta A, Kesavabhotla K, Baradaran H, et al. Plaque echolucency and stroke risk in asymptomatic carotid stenosis: a systematic review and meta-analysis. Stroke. 2015;46(1):91–7.PubMed

116.

Huibers A, de Borst GJ, Bulbulia R, Pan H, Halliday A. Plaque echolucency and the risk of ischaemic stroke in patients with asymptomatic carotid stenosis within the first Asymptomatic Carotid Surgery Trial (ACST-1). Eur J Vasc Endovasc Surg. 2016;51(5):616–21.PubMed

117.

Kotsis V, Jamthikar AD, Araki T, et al. Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. Diabetes Res Clin Pract. 2018;143:322–31.PubMed

118.

Park TH. Evaluation of carotid plaque using ultrasound imaging. J Cardiovasc Ultrasound. 2016;24(2):91–5.PubMedPubMedCentral

119.

Picano E, Paterni M. Ultrasound tissue characterization of vulnerable atherosclerotic plaque. Int J Mol Sci. 2015;16(5):10121–33.PubMedPubMedCentral

120.

Nicolaides AN, Kakkos SK, Kyriacou E, et al. Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification. J Vasc Surg. 2010;52(6):1486–1496.e1485.PubMed

121.

Pedro LM, Sanches JM, Seabra J, Suri JS, Fernandes e Fernandes J. Asymptomatic carotid disease—a new tool for assessing neurological risk. Echocardiography. 2014;31(3):353–61.PubMed

122.

Pahan K. Lipid-lowering drugs. Cell Mol Life Sci. 2006;63(10):1165–78.PubMedPubMedCentral

123.

Abramowicz M, Zuccotti G, Pflomm J-M. Metformin for prediabetes (reprinted from The medical letters on drugs and therapeutics, vol 58, pg 141, 2016). JAMA. 2017;317(11):1171–1.

124.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

125.

Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

126.

Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.PubMedPubMedCentral

127.

• Lekadir K, Galimzianova A, Betriu À, et al. A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform. 2017;21(1):48–55 This was an important study that perfromed the carotid atheroscleriotic plaque characterization using DL approach. PubMed

128.

Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Išgum I. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2018:1.

129.

Can we open the black box of AI? [https://www.nature.com/news/can-we-open-the-black-box-of-ai-1.20731]. Accessed 1 Oct 2018.

130.

Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging. 2017;10(10):e005614.PubMedPubMedCentral

Titel: A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography
verfasst von: Ankush Jamthikar
Deep Gupta
Narendra N. Khanna
Tadashi Araki
Luca Saba
Andrew Nicolaides
Aditya Sharma
Tomaz Omerzu
Harman S. Suri
Ajay Gupta
Sophie Mavrogeni
Monika Turk
John R. Laird
Athanasios Protogerou
Petros P. Sfikakis
George D. Kitas
Vijay Viswanathan
Gyan Pareek
Martin Miner
Jasjit S. Suri
Publikationsdatum: 01.07.2019
Verlag: Springer US
Erschienen in: Current Atherosclerosis Reports / Ausgabe 7/2019
Print ISSN: 1523-3804
Elektronische ISSN: 1534-6242
DOI: https://doi.org/10.1007/s11883-019-0788-4

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