Nobuyuki Kagiyama|Sirish Shrestha|Peter Farjo|Partho P. Sengupta
Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease
West Virginia University|West Virginia University|West Virginia University|West Virginia University
homejournal american heart associationvol 8 17artificial intelligence practical primer clinical research cardiovascular disease open accessreview articlepdfepubaboutview pdfview epubsections toolsadd favoritesdownload citationstrack citations shareshare onfacebooktwitterlinked inmendeleyredditdiggemail jump toopen accessreview articlepdfepubartificial intelligence practical primer clinical research cardiovascular disease nobuyuki kagiyama md phd sirish shrestha ms peter farjo md partho p senguptamd dm nobuyuki kagiyamanobuyuki kagiyama west virginia university heart vascular institute morgantown wv sirish shresthasirish shrestha west virginia university heart vascular institute morgantown wv peter farjopeter farjo west virginia university heart vascular institute morgantown wv partho p senguptapartho p sengupta correspondence partho p sengupta md dm west virginia university heart vascular institute 1 medical center dr morgantown wv 26506 email email address email protected west virginia university heart vascular institute morgantown wv originally published27 aug 2019httpsdoiorg101161jaha119012788journal american heart association 20198e012788introductionartificial intelligence ai begun permeate reform field medicine cardiovascular medicine impacting 100 million patients united states burden cardiovascular disease felt diverse array demographics1 2 meanwhile routine mediums multimodality images electronic health records ehr mobile health devices store troves underutilized data patient ai potential improve influence status quo capacity learn massive data apply knowledge distinct circumstances3 4 considerable information heart beat cardiovascular medicine definitely one fields embrace ai move toward personalized precise care5ai already woven fabric everyday life internet search engine email spam malware filtering uncovering fraudulent credit card purchases ai addresses individuals needs realms business entertainment technology unfortunately medicine including cardiology fully embraced revolution limited number aibased clinical applications available nevertheless promise towards routine implementation machine learning deep learning seen exponential surge cardiovascular publications past decade6 7 methods proven beneficial variety complex areas including echocardiogram interpretation diastolic dysfunction grade stratification8 9 us food drug administration already approved several devices utilize ai features10 imagine coming work finding system analyzed patients sleeping laboratory data imaging results symptoms mobile device data calculate risk cardiovascular events death hospitalization whether medications adjustedaddedremoved whether referred examination system presents reasons recommendations confident good given experienced physicians may allow spend time shared decision making patients objectively compassionately although currently several barrierschallenges adoption ai clinical practice undoubtedly ai drive current healthcare practice towards individualized precisionbased approach next several years therefore general understanding ai techniques clinicians researchers cardiovascular medicine paramount review article describe fundamentals ai clinicians researchers understand definition principles interpret apply ai cardiovascular research limitations future perspectivesbasic knowledge aiterminologiescurrently term ai glamorous medical field however confusion misunderstanding terms techniques ai broad ambiguous term describes computational programs simulate mimic human intelligence problem solving learning ai indicate generalpurpose ai general ai system selfsufficient possesses cognition comparable humans yet general ai appeared applied ai specific ai specialized dedicated purpose ai currently available11 machine learning 1 subfield applied ai automatically discovers patterns data without using explicit instructions12 machine learning machine learns data performs tasks based learned model whereas simple computer programs perform tasks according preset rules created based human experience knowledge applied ai machine learning used interchangeably medical research context since machine learning accommodates ai technologies medical research setting includes various algorithms prediction classification tasks perform well complex big data deep learning subfield machine learning algorithms use deep multiple layered neural networks originally inspired structure human brain13 neural networks designed currently however work substantially differently human neuron functions past decade deep learning increasingly used shown outperform machine learning techniques various fields image recognition voice recognition game playing competition image classification emergence modern deep learning convolutional neural network cnn 2012 big impact scene among traditional machine learning methods showing error rates 26 reasonably good time cnn showed outstanding error rate 15314 image classification using cnn improving current error rate 3 surpasses abilities human eye15 successes attributed capability extract important features enormous data iterative data processing contrast traditional machine learning algorithms require degree arbitration analysts eg feature selection feature engineering process selecting creating features variables make algorithms perform better deep learning generally selfdirected implemented example order deal chest xray images traditional machine learning analysts must first collect measurements parameters size heart presence congestion spreadsheet process categorization loses information time consuming instead deep learning feed raw image information extract key patterns images itselfsupervised unsupervised reinforcement learning another group terms describe way machine learns data figure 1 supervised learning algorithms learn data information outcome ground truth develop prediction model typical tasks handled supervised learning classification regression classification task predicting categorized outcome 1year mortality yes disease diagnosis3 16 using given parameters regression predicts value eg predicting echocardiographic early diastolic left ventricular relaxation velocity value ecg information contrary unsupervised learning require ground truth explores data find hidden patterns associations8 common tasks unsupervised learning clustering dimensionality reduction clustering task divide objects groups similar characteristics dimensionality reduction also performed supervised manner task reduce dimensionality number variables data keeping principal variables explain data tasks aim identify phenotypes inferring patterns data set without known labeled outcome reinforcement learning technique algorithm trained learn action gains maximum reward situation technique widely used decisionmaking gaming programs alphago first program beat professional go player also used reinforcement learning learn best actions game go17download figuredownload powerpointfigure 1 supervised unsupervised reinforcement learning machine learning tasks categorized supervised unsupervised reinforcement learning supervised learning used prediction classification regression whereas unsupervised learning aims reveal hidden patterns data reinforcement learning another way learning algorithm learns best action based consequences well suited game theory control theory however significant role clinical research requires simulating many wrong actions learn dimensionality reduction also performed supervised mannerbig data machine power deal itthe concept methodologies ai techniques brandnew however sudden prosperity ai past decade occurred emergence big data evolution computing power well development deep learning techniques previously lack data big enough train ai one bottlenecks ai development however limitation disappeared big data became available popularization internet digital information stored smart devices internet connection transfer data network without requiring human interaction increased influx usable data analyzing big data using ai requires huge amount computational power graphic processing units originally invented perform specialized tasks gaming graphics fast parallel computing power fitted well deep learning tasks13 recent years graphic processing units become affordable computing power continued grow exponentially currently researchers also appreciate cloudbased infrastructure service recently called machine learning service amazon web services microsoft azure ml google cloud ml provide power graphic processing units various ai applications limitless data storage cloud platform furthermore machine learning service provide automated machine learning systems google cloud auto ml bigml various machine learning algorithms require nonetominimal coding available systems used simple graphic user interfaces may better choice researchers novices machine learning thus resources ai research become available general clinicians researchersdifference ai traditional statisticsstatistics standard method medical research purpose showing benefit new therapies predicting prognoses identifying risk factors revealing disease mechanisms interestingly significant overlaps techniques methodologies domain traditional statistics ai example logistic linear regression models medical researchers familiar also techniques machine learning perhaps fundamental difference philosophies statistics science estimates explains data whereas ai machine learning aims achieve practical prediction data hand example linear regression models important parameters interest statistics coefficients weights term goodnessoffit explain data hand ai focuses prediction unknown data accordingly primary concern ai research model performance test set used process model training presented different terms statistics recall sensitivity fmeasure harmony sensitivity positive predicted value confusion matrix type crosstabulation table fact calculation process less interest ai complex ai models even provide coefficient metrics interpretability therefore usually ai requires fewer assumptions data often uses complex nonparametric models requires much data simple parametric models frequently used traditional statistics traditional statistics perform well certain hypothesis suitablesized data set ai generally outperforms statistical methods prediction large complex dataanother important consideration especially important medicine ai techniques unlike sophisticated statistical analysis suggested provide causal inferences actually renewed enthusiasm using machine learning purpose evolving paradigm however verified cautiously existing domain knowledge pathophysiology disease mechanisms support results ai analysis capability working various data structures also important strength ai compared traditional research discussed figure 2 summarizes differences traditional medical research statistics research using aidownload figuredownload powerpointfigure 2 pipelines medical research using traditional statistics ai traditional medical research formulates hypothesis first tests using statistical analysis medical research using ai hypothesisfree datadriven compared traditional statistics ai deal various types data including unstructured data images signals ehr contrast traditional medical research focuses validation hypotheses understanding causality mechanisms main goal research using ai predict new data identify hidden pattern data ai indicates artificial intelligence ehr electronic health recordrepresentative algorithms aimachine learning deep learning consist multitude algorithms table 1 summarizes brief descriptions basic machine learning algorithms used different tasks currently ensemble learning deep learning described mainstay algorithms ai ensemble learning machine learning method combines multiple weak learners algorithms decision tree logistic regression table 1 obtain good prediction boosting bagging stacking 3 main methods ensemble learning18 boosting multiple weak learners combined series trained subsequently considering errors preceding algorithms reduced bias bagging method multiple weak learners trained parallel results algorithm combined make final output small variance stacking method results weak learners used input another machine learning algorithm meta learner ensemble learning methods work well combining various types simple algorithms generally outperform single machine learning algorithmtable 1 representative machine learning algorithmsalgorithmdescriptionuselogistic regressionan algorithm estimates probability dichotomized outcome multiple covariates using logistic functionclassificationdecision treea flow chartlike algorithm divides data branches considering information gain final branches represent output algorithm class valueclassificationregressionsimple neural networkan algorithm inspired human brain architecture layers consisting nodes connected one another edges weighted per training resultsclassificationregressionk nearest neighbora simple algorithm classifies observations comparing k examples exist nearest locations examples similar featuresclassificationregressionsupport vector machinesupport vector machine draws boundary line maximizes margins class new observations classified using lineclassificationregressionk meansa clustering method makes k clusters observation belongs cluster mean nearest locations observationclusteringhierarchical clusteringa type cluster analysis builds dendrogram hierarchy clusters pairs clusters merged form clusters move hierarchy agglomerative approachclusteringprincipal component analysisan algorithm converts high dimensional data lower dimensional data keeping important information much possible orthogonal transformationdimensionality reductiondeep learning outperforms traditional machine learning methods analyzing complex data images texts unstructured data general deep learning consists input layer hidden layers output layer input output layers indicate original data output algorithm respectively multiple hidden layers raw input gradually converted abstract essential features represent original data figure 3 image recognition input layer indicates raw pixels image first layers identify simple features image edges lines succeeding layers identify somewhat complex features ears eyes tails finally last layers recognize features cats dogs deep learning extracts key features raw unstructured data returns outputs classification regressiondownload figuredownload powerpointfigure 3 structure deep learning deep learning consists input layers hidden layers output layers multiple hidden layers raw input gradually converted abstract essential features represent original data image recognition input layer indicates raw pixels image first layers identify simple features image edges lines succeeding layers identify somewhat complex features ears eyes tails finally last layers recognize features cats dogs deep learning extracts key features raw unstructured data returns outputs classification regressionexamples ai cardiovascular datastructured dataexamples ai studies various data sources summarized table 2 currently medical research done using structured data labeled properly organized formatted fields tabular formtable 2 examples ml cardiovascular researchdata structureyearfirst authorjournalconferencetaskmodelsummarystructured data2016motowanieur heart jclassification prognostic predictionensembleusing 69 clinical ct parameters 10 030 cad patients ml model predicted mortality better traditional statistics2018kakadiarisjahaclassification prognostic predictionsvmusing 9 parameters consist accaha risk calculator ml model showed better prediction original accaha risk score2016narulajaccclassification diagnosis hcmensemble svm rf annusing clinical echocardiographic parameters ml algorithms discriminated hcm ath 87 sensitivity 82 specificity2019lancasterjacc cv imagingclusteringhierarchical clusteringusing echocardiographic parameters guidelines recommend assessment lvdd hierarchical clustering identified clusters discriminate patient prognosis better guidelinesbased classification2019casaclangverzosajacc cv imagingclustering dimensionality reductiontopological data analysistopological data analysis able visualize patientpatient similarity network created 4 parameters relative location patients network associated disease phenotypes prognosisunstructured dataechocardiographic images2018zhangcirculationclassification automatic interpretation echocardiographycnnusing 14 035 echocardiograms cnn enabled automatic classification views identification chambers measurements cardiac volumes discrimination diseases healthy controls see text detailsmri images2019zhangradiologyclassification prediction mi nonenhanced mrilstmoptical flowin 212 patients 87 controls algorithms able detect chronic mi validated lge 90 sensitivity 99 specificity using nonenhanced cine mrict images2016shandmimed image analclassification coronary artery calcium voxelcnnusing 3d cta 250 patients localization volume interest using 3 cnns 2 cnns used classify voxels calcium noncalcium agatston score calculated based voxel classification showed excellent agreement reference standard accuracy 83ecg signals2019hannunnat medclassification arrhythmia detectiondnnusing 91 232 singlelead ecg trained algorithm showed better prediction 12 types heart rhythm cardiologist fmeasure 084 vs 078heart sound signals2016potes2016 cincclassification normal abnormal heart soundadaboostcnncombination adaboost cnn showed 942 sensitivity 778 specificity identifying abnormal heart sound physionetcinc data setehr2019mallyaarxivclassification prognostic predictionlstmusing 23 000 patients timeseries data lstm algorithm successfully predicted onset heart failure 15 mo advance auc 091ehr medical letters text2019dillereur heart jclassification diagnosis symptoms prognosiscnnlstmusing natural language processing diagnosis accuracy 91 symptoms 906 extracted medical letters also prognostic prediction using data useful hr 340acc indicates american college cardiology aha american heart association ann artificial neural network ath athlete auc area receiveroperatingcharacteristic curves cad coronary artery disease cnn convolutional neural network ct computed tomography cta computed tomography angiography dnn deep neural network hcm hypertrophic cardiomyopathy hr hazard ratio lge late gadolinium enhancement lstm long short time memory lvdd left ventricular diastolic dysfunction mi myocardial infarction ml machine learning mri magnetic resonance imaging rf random forest svm support vector machinemotwani colleagues16 investigated predictive ability machine learning algorithms structured data predict 5year mortality large population 10 030 patients coronary artery disease enrolled confirm coronary ct angiography evaluation clinical outcome study used ensemble learning analyze 69 parameters data set using 10fold crossvalidation explained later trained machine learning algorithm predict mortality supervised learning training set tested predictive ability using validation set features selected fold using information gain found area receiveroperatingcharacteristic curves prediction 5year mortality significantly better machine learning algorithm 079 traditional risk scores 061064structured data also applied unsupervised machine learning algorithm effortlessly efficiently recently group19 applied novel data analytics technique called topological data analysis build patientpatient similarity network utilized underpinning mathematics underlying unsupervised machine learning topological data analysis framework machine learning borrows amalgamates various machine learning algorithms understand fundamental properties shape complex data study applied technique understand phenotypic representation pattern left ventricular responses progression aortic stenosis topological data analysis along dimensionality reduction formed loop segregating mild severe disease opposite ends linking moderate disease routes preserved reduced left ventricular ejection fraction figure 4 interestingly upon supplementing data followup succeeding aortic valve replacement therapy patients accurately captured traversed severe mild moderate aortic stenosis similar model applied murine model reversetranslational study showed similar distribution separating mice high peak aortic velocities 1 end low velocities connecting via moderately severe peak aortic velocities top bottom loopdownload figuredownload powerpointfigure 4 topological data analysis patients topological data analysis enables integration multidimensional complex data visualization hidden patterns data node represents 1 patients similar echocardiographic parameters nodes including similar patients connected edges panel colored 1 parameter written top right color nodes represents mean value parameter nodes although network created parameters aortic stenosis preserved reduced lv function systolic diastolic segregated different regions indicates aortic stenosis av aortic valve lv left ventricle reprinted casaclangverzosa et al19 permission copyright 2019 elsevierhowever machine learning always superior traditional statistics frizzell et al20 studied 56 477 patients admitted hospitals older 65 years age data set included 100 candidate variables performance prediction 30day rehospitalization rate compared among several machine learning methods including ensemble models despite large data set logistic regression model achieved better performance complex machine learning models importantly even performance logistic model modest c statistics 0624 suggesting many unmeasuredunknown important variables contribute outcome complex machine learning models fail outperform simple models absence several contributing variablesunstructured dataunstructured data data stored without wellorganized structure applied traditional statistical tabular databases medical field textual information ehr medical images audio visual clips examples data considered unstructured emergence machine learning opened avenues effectively analyze data thought contain 80 90 potentially usable information21 huge resource medical researchmedical imagestraditionally difficult computers automatically deal medical images enormous varieties disease patterns physicians exclusively read interpret analyze variety medical images processes timeconsuming one bottlenecks clinical practice deep learning especially cnn derivative neural networks becoming game changer process medical image analysis capability learn features pixels classify segment objects images 22 23zhang et al24 used cnn develop pipeline automation following echocardiographic tasks 1 view classification 2 image segmentation 3 measurements cardiac structure function 4 discrimination diseases study deep learning algorithm trained supervised manner classify images 1 23 types views normal parasternal long axis remote parasternal long axis etc using 70 000 preprocessed still images generated 277 echocardiograms 5fold crossvalidation algorithm could excellently distinguish broad subclasses one another overall classification accuracy 84 image segmentation another algorithm trained segment cardiac chambers 5 views apical 2 3 4chambers parasternal long shortaxis views respectively supervised learning using 124 214 images per single view algorithms able segment areas individual cardiac chambers excellent overlap humanannotated areas chambers using autoannotated chambers calculated chamber volumes left ventricular ejection fraction left ventricular mass agreed manually measured values median absolute deviations 1517 finally trained algorithms disease classification hypertrophic cardiomyopathy n260 matched normal controls n2064 cardiac amyloidosis n81 controls n771 pulmonary artery hypertension