Artificial Intelligence and Machine Learning for Healthcare: Vol. 1: Image and Data Analytics

239 Pages·2022·6.296 MB·English

by  Chee-Peng Lim, Ashlesha Vaidya, Yen-Wei Chen, Tejasvi Jain, Lakhmi C. Jain

Intelligent Systems Reference Library 228
Chee-Peng Lim · Ashlesha Vaidya ·
Yen-Wei Chen · Tejasvi Jain ·
Lakhmi C. Jain   Editors
Artificial
Intelligence
and Machine
Learning for
Healthcare
Vol. 1: Image and Data Analytics
Intelligent Systems Reference Library
Volume 228
SeriesEditors
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LakhmiC.Jain,KESInternational,Shoreham-by-Sea,UK
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· ·
Chee-Peng Lim Ashlesha Vaidya
· ·
Yen-Wei Chen Tejasvi Jain Lakhmi C. Jain
Editors
Artificial Intelligence
and Machine Learning
for Healthcare
Vol. 1: Image and Data Analytics
Editors
Chee-PengLim AshleshaVaidya
InstituteforIntelligentSystemsResearch DepartmentofGeriatricandRehabilitation
andInnovation Medicine
DeakinUniversity RoyalAdelaideHospital
WaurnPonds,VIC,Australia Adelaide,SA,Australia
Yen-WeiChen TejasviJain
CollegeofInformationScience HCLTechnologiesLimited
andEngineering Noida,India
RitsumeikanUniversity
Kusatsu,Shiga,Japan
LakhmiC.Jain
KESInternational
Shoreham-by-Sea,UK
ISSN 1868-4394 ISSN 1868-4408 (electronic)
IntelligentSystemsReferenceLibrary
ISBN 978-3-031-11153-2 ISBN 978-3-031-11154-9 (eBook)
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Preface
Advancesindigitalandcomputingtechnologieshavedrivenarapidgrowthinartifi-
cialintelligence(AI)andmachinelearning(ML)andtheirapplicationstoavarietyof
sectors,includingthemedicalandhealthcaredomains.Nowadays,electronicmedical
recordsanddiagnosticimagingofpatientscanbereadilycollectedandanalysedby
utilising AI and ML systems to derive insights and assist medical professionals
in making accurate clinical decisions. As a result, effective and efficient services
pertainingtodiseasesdiagnosisandprognosis,treatments,rehabilitation,andother
patient-caretaskscanberealisedforimprovinghealthcaredelivery.
Inthiseditionon“ArtificialIntelligenceandMachineLearningforHealthcare”,a
totalof21chaptersfromresearchersandpractitionersaroundtheworldarepresented.
Divided into two volumes, the first covers selected AL/ML-based image and data
analyticsolutionstoaddressavarietyofmedicalandhealthcareproblems,whilethe
secondpresentsseveralcurrentmethodologiesandfuturetrendsinadvancementof
AL/MLforhealthcare.
Thereare11chaptersinthisfirstvolume.AgeneraloverviewofAI/MLisgiven
in the first chapter. Other selected chapters describe various AL/ML models, e.g.
supportvectormachine,convolutionalneuralnetworks,decisiontrees,graph-based
models,forhealthcareresearch,development,andapplicationsfromtheimageand
dataanalyticperspectives.Asummaryofeachchapterinthisvolumeisasfollows.
Belciug provided an overview of AI in healthcare. The importance of AI in
medicine is first elucidated. A number of commonly used AI and ML models are
described,whichincludethedecisiontree,randomforest,Bayesianclassifier,multi-
layerperceptron,andconvolutionalneuralnetworks.Theadvantagesandlimitations
of AI for healthcare are explained, and several successful AI-based applications
aredescribed.UsefulresourcespertainingtoAIandMLforhealthcareandrelated
domainsarealsopresented.
Precision medicine, a concept that focuses on customisation of healthcare, e.g.
diagnosis,treatmentandfollow-up,forindividualpatients,isarecenttopicofinterest.
Jinetal.developedaradiogenomicsmethodtodiscoverimagingbiomarkersrelated
tobreastcancer.Basedonmagneticresonanceimaging(MRI),asequentialforward
v
vi Preface
floatingselectiontechniquecoupledwithasupportvectormachine(SVM)isdevel-
oped for predicting genetic test results with respect to breast cancer. Good results
arereportedanddiscussed.
Data-driventechniquesareusefulforprocessingMRItoimproveimagequality
and temporal resolution. Placidi et al presented a GReedy Adaptive Data-driven
Environment(GRADE)forintelligentradialsampling.Itusesthepowerspectrum
of a reconstructed image and AI-based super-resolution strategies in an iterative
acquisition/reconstruction process to reduce data redundancy. The method leads
to high-quality images as compared with those from other under-sampling radial
modalities.
To facilitate automatic and accurate liver tumour detection in multi-phase
computedtomography(CT)images,Jainetal.proposedadomainadaptation-based
methodtoovercomethelackofannotateddataissueintrainingdeeplearningmodels.
The domain shift problem in different phases of liver CT images is discussed. To
addresstheassociateddomaingap,adversariallearningschemewithananchor-free
objectdetectorisemployed.Inaddition,amaximumsquarelossfunctionformid-
leveloutputfeaturemapsisformulatedtoimprovetheperformance.Theproposed
domainadaptationmethodoutperformsothermodelstrainedwithnormalsettingsin
severalexperimentalstudies.
On the other hand, to tackle the similar issue on unavailability of large sets of
labelled data for supervised learning, Andreini et al. developed a new approach to
generate synthetic images. The method can be applied to semantic segmentation,
wherethegeneratedimagescanbeusedtoaugmentrealdatasetsformodeltraining.
A multi-stage approach is designed, whereby the generation procedure is carried
out in multiple steps, in order to simplify the overall generation task. Based on
retinal fundus and chest X-ray images, the multi-stage approach is effective and
computationallyeconomicingeneratinghighresolution,realisticimagestobeused
fortrainingdeeplearningmodels.
Arrhythmia is a fatal cardiovascular disease with the symptoms of fast heart-
beat,slowheartbeat,orirregularheartbeatrhythms.SiaoandRamliinvestigatedthe
convolutional neural network (CNN) for arrhythmia classification based on elec-
trocardiogram(ECG)signalsandimproveditsperformancebyleveragingthelong
short-term memory (LSTM) model. ECG signals are pre-processed with filtering,
segmentation,andmedianwaveselection.TheresultsindicatethattheCNN-based
solution is useful for atrial fibrillation diagnosis, contributing towards healthcare
advancement.
Colorectal cancer is a leading cause of mortality, and early and accurate detec-
tion of polys in colonoscopies plays a significant role in increasing the survival
rate of patients. Automatic polyp detection systems are useful tools for assisting
medicaldecision-making.Nannietal.devisedanensembleofCNNmodelsanddata
augmentation methods for polyp segmentation. The results from five benchmark
problemsindicateefficacyofthedevelopedensembleapproachforsegmentingpolys
incolonoscopyimages.
Preface vii
To help patients with Autism Spectrum Disorder (ASD) in communication,
de Luise et al. analysed head bouncing actions recorded in diverse circumstances.
Ashorttrackingandlightweightprocessingapproachisappliedtoavideotestset
concerningautisticindividuals.Aworkflowforprocessingstimmingandcollecting
relevantmetadataisdevised.MLmodelsareadoptedfortuningthetrackingprocess.
Theresultsindicategoodprecisionindetectingspecificindividual’sautisticverbal
behaviours.
Patients in medical wards exhibit multiple pathologies, which lead to burdens
in terms of activities, risks, and costs for the health system. Landa et al. investi-
gatedtheuseofAI-basedmodellingmethodologiespertainingtomedicalwardsina
mediumhospitalsetting.Unsupervisedmethodologiescombiningtheself-organising
map(SOM)andK-meansclusteringareexploited.Patientsareorganisedinspecific
diagnosis-relatedgroups,andtheSOMisleveragedtoidentifytheunderlyingcluster
features.Thedevelopeddata-drivensolutionoffersaneffectivemethodologytostudy
resourceutilisationinhospitalenvironments.
Pridayetal.conductedastudytocreateperson-centredcaremeasuresforhearing
rehabilitation. A dashboard, which is an AI-based infrastructure, is designed and
developedtobringtogethertherelevantdatasourcesforanalysisandinterpretation
with respect to defined goals. The process to ensure the right patient data samples
whicharecollectedandfedintothesystemtoachievemeaningfulcaremeasuresis
described,whilethebarriersfacedwhencollatingandinterpretingthemeasuresof
thedashboardinpracticeareexplained. ImplicationsontheimplementationofAI
forimprovingperson-centredcarearediscussed.
Graphs offer a ubiquitous and expressive form of data representation, which is
useful for elucidating information on entities and their interrelationships. Bongini
et al. exploited the properties of Graph Neural Networks (GNNs) in processing
datawithminimallossofstructuralinformationtotacklethreebiologicalproblems.
Specifically, graph-based models for the prediction of protein–protein interfaces,
prediction of drug side effects, and generation of molecular graphs are developed.
Thefindingsareinlinewiththeoreticalexpectationsontheexpressivecapabilitiesof
GNNs,whichindicatemanypossiblefuturedirectionsforresearchandapplication
ofnewandexistinggraph-basedmodelsforsolvingbiologicalproblems.
Theeditorswouldliketothankallauthorsandreviewersfortheircontributionsand
theSpringereditorialteamfortheirhelpinthispublication.Thechapterspresented
in this volume are just a small selection to cover the rapidly changing AI and ML
paradigmsinthehealthcaredomain,aimingtoinspireresearchersandpractitioners
tofurtheradvanceAIandMLtechnologiesforadvancingthehealthcaresector.
WaurnPonds,Australia Chee-PengLim
Adelaide,Australia AshleshaVaidya
Kusatsu,Japan Yen-WeiChen
Noida,India TejasviJain
Shoreham-by-Sea,UK LakhmiC.Jain
May2022
Contents
1 AnIntroductiontoArtificialIntelligenceinHealthcare ........... 1
SmarandaBelciug
1.1 IntroductiontoArtificialIntelligence ........................ 2
1.2 ArtificialIntelligenceinHealthcare ......................... 3
1.2.1 NaturalLanguageProcessing(NLP)Technology ...... 5
1.2.2 MachineLearning(ML)Algorithms ................. 5
1.2.3 ArtificialNeuralNetworks ......................... 6
1.2.4 BayesianClassifier ................................ 6
1.2.5 Classification/DecisionTrees.RandomForest ......... 7
1.2.6 SurvivalRegressionModels ........................ 8
1.2.7 ClusterAnalysis .................................. 8
1.3 AdvantagesofArtificialIntelligenceinHealthcare ............ 9
1.4 LimitationsofArtificialIntelligenceinHealthcare ............ 11
1.5 Successful Applications of Artificial Intelligence
inHealthcare ............................................ 12
1.6 Conclusions ............................................. 13
Appendix ..................................................... 13
References .................................................... 15
2 Radiomics:ApproachtoPrecisionMedicine ..................... 17
ZeJin, TaiguangYuan, YukikoTokuda, YasutoNaoi,
NoriyukiTomiyama,andKenjiSuzuki
2.1 Introduction ............................................. 18
2.2 MaterialsandMethods .................................... 19
2.2.1 BuildingofaDatabase ............................ 20
2.2.2 SegmentationofTargetVolume ..................... 21
2.2.3 ExtractionandSelectionofUsefulRadiomics
Features ......................................... 22
2.2.4 Model Building Based on Machine Learning
Technologies ..................................... 25
2.3 ResultsandDiscussion .................................... 25
ix
x Contents
2.4 Conclusions ............................................. 26
References .................................................... 28
3 ArtificialIntelligenceBasedStrategiesforData-DrivenRadial
MRI ......................................................... 31
GiuseppePlacidi, LuigiCinque, FilippoMignosi,
MatteoPolsinelli,andAlessandroSciarra
3.1 Introduction ............................................. 32
3.2 RelatedWork ............................................ 34
3.2.1 SparseSamplingStrategies ......................... 34
3.2.2 ContributionoftheManuscript ..................... 37
3.3 ProblemStatementandFrameworkDescription ............... 38
3.3.1 Relationship Between Radial Projections
andImage ....................................... 38
3.3.2 ImageReconstruction,ResolutionandNoise .......... 41
3.3.3 Super-Resolution ................................. 44
3.3.4 FrameworkDetails ................................ 46
3.3.5 NoiseThresholdT ................................ 49
3.4 ResultsandDiscussion .................................... 50
3.5 Conclusion .............................................. 53
References .................................................... 55
4 UnsupervisedDomainAdaptationApproachforLiverTumor
DetectioninMulti-phaseCTImages ............................ 61
RahulKumarJain, TakahiroSato, TaroWatasue,
TomohiroNakagawa, YutaroIwamoto, XianhuaHan,
LanfenLin,HongjieHu,XiangRuan,andYen-WeiChen
4.1 Introduction ............................................. 63
4.1.1 Domain-ShiftProblem ............................. 63
4.1.2 DomainAdaptation ............................... 65
4.2 DomainAdaptationUsingAdversarialLearning .............. 66
4.2.1 Anchor-freeDetector .............................. 66
4.2.2 Proposed Multi-phase Domain Adaptation
Framework Using Adversarial Domain
ClassificationLoss ................................ 67
4.3 ProposedMulti-phaseDomainAdaptationFramework
UsingAdversarialLearningwithMaximumSquareLoss ....... 69
4.3.1 MaximumSquareLoss ............................ 70
4.3.2 OverallFrameworkwithAdversarialDomain
ClassificationandMaximumSquareLoss ............ 70
4.4 Experiments ............................................. 71
4.4.1 ImplementationDetails ............................ 71
4.4.2 Dataset .......................................... 71
4.4.3 Evaluation ....................................... 72
4.4.4 Results .......................................... 73