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Machine Learning in Dentistry Ching-Chang Ko Dinggang Shen Li Wang Editors Machine Learning in Dentistry Ching-Chang Ko • Dinggang Shen (cid:129) Li Wang Editors Machine Learning in Dentistry Editors Ching-ChangKo DinggangShen OhioStateUniversity SchoolofBiomedicalEngineering Columbus,OH,USA ShanghaiTechUniversity Shanghai,Shanghai,China LiWang UniversityofNorthCarolina atChapelHill ChapelHill,NC,USA ISBN978-3-030-71880-0 ISBN978-3-030-71881-7 (eBook) https://doi.org/10.1007/978-3-030-71881-7 ©SpringerNatureSwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewhole orpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway, andtransmissionorinformationstorageandretrieval,electronicadaptation,computersoftware, orbysimilarordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthis publicationdoes not imply,evenintheabsenceofa specificstatement,thatsuchnamesare exemptfromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationin thisbookarebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublisher northeauthorsortheeditorsgiveawarranty,expressedorimplied,withrespecttothematerial containedhereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremains neutralwithregardtojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG. Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface MachineLearninginDentistry Machine learning (ML) is a branch of artificial intelligence (AI), which is growing fast in both the research community and the general public. In contemporary dentistry, digital technologies such as cone beam computer tomography(CBCT),intra-oral3Dscan,3Dprinting,andpersonalizedtreat- mentplanninghavecometoplayalargerroleinbothresearchandpractice. Thesetechnologiesholdpromiseformorepredictable,objective,andeffective treatmentwhilereducingiatrogeniccomplications.Butforchangetooccur, datascientistsanddentalexpertsfromvariouscoherentdomainsmustwork togetherto developbig dataanalyticsthathavetranslationalvaluesandcan bedeployedinoralhealthcareandlifesciences. Inthepast,thediagnosticinformationwascollectedbyclinicalinterviews, plastermodels,andchair-sideobservations.Theinformationwastheninter- pretedbyexpertstoderiveatreatmentplanforimplementation.Thevariations ineachstepofdiagnosis,treatmentplanning,andtreatmentimplementation arehugeandpurelyempirical,dependingupondoctor’sexperience.Assuch, conventional dental education is largely dependent upon repetitive training of clinician’s eyes, hands, and judgment (critical thinking) to minimize the variationinstandardcare.Theamountoftimespentintheprofessionalschool andinlifelongcontinuededucationisexcessive,andtherearenoguarantees thateveryindividualwillreachthesamestandardcredential. Today,thenewtechnologiescanreplacehuman’seyesandhands,andAI canteach humanlearningprocessestothemachinetoclosetheeducational gaps. Many healthcare providers have integrated digital technologies into practice workflows, which reducethe dependenceon handskills andvisual recognitions.Computersoftwareisinitsinfancytointegratethesetechnolo- giestoimproveobjectiveperception,cognition,andactionwithexperience. Nevertheless, ML in dentistry is about developing a personalized precision practice and improving diagnosis and treatment planning from big data, literature, experience, and outcome-dependent learning. These also include the interrogationof large, complex‘omics datasets such as genetic lociand biomarkersforcraniofacialdisorders. Thepurposeofthisbookistoreviewthecurrentclinicalsystemsanddental researchinvolvingmachinelearningtoolsanditsassociationsamongvarious dentalspecialties.Theexamplesshowninthisbookrepresentasampleofthe opportunities and challenges for what is possible with the ML approach in v vi Preface contemporarydentistry.Wehaveinvitedexpertstocontributetheirreviewin thefollowingfourareas. MachineLearningforDentalImaging Dentists often read radiographs to detect craniofacial anomalies for identi- fying potential problems. However, mistakes can be made throughimaging examination and the diagnostic accuracy is dependent on the experience of the dentist. With a 3D image, this process turns out to be a stressful task becauseitisbasedonbigdataanduserexperience.Byusingmachinelearning to augment the image, diagnosis can be more accurate and objective, and treatmentscanbepersonalized. Acomputerdoesnotgettiredfromgruelingtasks,anditcantakeinagreat deal of data and process the information very quickly. First, the computer takes in a set of different radiographs labeled for healthy and unhealthy anatomies. Then, once the computer is given data of experts’ diagnoses, it can classify the patterns that are associated with certain disease or healthy anatomies. Thus, given a new patient’s radiograph, the machine can easily matchitwithpatternsthatwerefoundinthetrainingsetofexpertdiagnostics. There are already machine learning programs that are able to detect 2D panoramic features on the market, like denti.ai or dentistry.ai. In this book, wedelvedeeperintotherolethecomputerdoesfor3Dimagingsegmentation andlandmarkdetectionusingmachinelearninganddeeplearning.Chapters 1–5 describe the algorithms and provide examples for augmentation of craniofacial skeletal structures, facial surface recognition, and applications insimulationoforthognathicsurgery. MachineLearningforOralDiagnosisandTreatmentPlanning When conducting a medical diagnosis, a dentist would take in the patient’s chief complaint, history, conversation,and radiographs. Some of this information can be descriptive, and because of that, the dentist must be very experienced and in top condition to pay close attention to every word and its nuances from a patient’s narrative. Because it assembles these words to makeagooddiagnosis,dentistsmayeasilymisdiagnosetheirpatient.In2011, dentistswerefoundtohavea43%misdiagnosisrateoforallesions(Kondori etal.).Thisnumberisfrighteninglyhighandindicatesroomforimprovement. In medicine, there are various commercial software for auto-annotations of pathology images. Instead, the book focuses on clinical diagnosis and treatmentplanningusingAI. Natural language algorithm is part of machine/deep learning, which can support searching for key words and identify frequency and patterns of de- scriptivestatements.Chapters6–9providestate-of-the-artAIfororthodontic diagnosisandtreatmentplanning.Thisincludesfacialrecognition,cephalo- metric analysis, chart analysis, automated problem list and treatment planning,characterizationofcraniofacialanomalies,anddecisionoforthodontic Preface vii tooth extraction.The AI couldsave time andstreamline anyprocesseswith improvedaccuracyinthefuture. MachineLearningandDentalDesigns/Outcome Assessment Emergingintra-oralscannerand3Dprintinghaveimprovedworkflowofor- thodonticpractice.Todate,theuseofdigitalsurfacemodelhashadadramatic expansion from diagnosis to treatment planning, for example orthodontic aligner therapy.Automatic tooth segmentation from thedental arch already benefits the design of restorations, tooth setup and alignment, personalized treatment, and patient-centered outcome assessment. Chapter 10 updates currentAItechnologiesfortoothisolationfromthe3Dsurfacemodel.Dental specialties havebeenin theforefrontofusingartificialintelligenceanalytic strategies to leverage the advantages of “big data” to personalize treatment interventions, such as aligner therapy. Chapter 11 provides an overview of machinelearningandhowitcanbeappliedin assessing outcomes.Specifi- cally,wewillfocusonrecentadvancesincraniofacialgenomicsandoutcomes pertainingtoimages. MachineLearningSupportingDentalResearch Oralsystemichealthistightlyassociatedwithgooddentalresearch.Clinical researchistodevelopevidence-basedguidelinesandtheirpotentialimpacton thehealthandwell-beingoflargeportionsofthepopulation.Thetranslational valueofthesestudiesreliessignificantlyontheeffectivenessofthesystemic review. Chapter 12 provides a review of using ML for a systemic review. The authors also discuss about the shortcomings and potential pitfalls of employingartificialintelligence(AI)toareviewprocessthathasbeenmostly drivenbyhumanexpertsinthepast. Human genomics was one of the first areas of research to use big data. In Chap. 13, the researchers introduce how the machine learning and deep learningalgorithmsareusedindentalomicsstudiesforassociationanalysis between SNPs and complex diseases, CNV and SNV calling, and DNA methylation data. In the end, we discuss the potential developing direction ofmachine(deep)learninginbiomechanicsinoralhealth(Chap.14). WethankSpringerandallthecontributorsfortheircollectivearticles,Dr. Tai-HsienWuandDr.ChunfengLianfortheliaisontasktocommunicatewith thecontributors,andMs.Devanforcarefulmanagementofthesubmissions. ParticularthanksalsogotoDrs.Pastewait, Piers,andChienandMs.Zheng forreviewingthemanuscripts.Webelievethisbookisjustthebeginningofa seriesofbooksonthetopicofAIincontemporarydentistry. Columbus,OH Ching-ChangKo Shanghai,China DinggangShen ChapelHill,NC LiWang Contents PartI MachineLearningforDentalImaging 1 Machine Learning for CBCT Segmentation of CraniomaxillofacialBonyStructures...................... 3 ChunfengLian,JamesJ.Xia,DinggangShen,andLiWang 2 Machine Learning for CraniomaxillofacialLandmark Digitizationof3DImaging............................... 15 JunZhang,MingxiaLiu,LiWang,ChunfengLian,andDinggangShen 3 Segmenting Bones from Brain MRI via Generative AdversarialLearning ................................... 27 XuChen,ChunfengLian,LiWang,Pew-ThianYap,JamesJ.Xia, andDinggangShen 4 Sparse DictionaryLearning for3DCraniomaxillofacial SkeletonEstimationBasedon2DFacePhotographs ........ 41 Deqiang Xiao, Chunfeng Lian, Li Wang, Hannah Deng, Kim-HanThung,Pew-ThianYap,JamesJ.Xia,andDinggangShen 5 MachineLearningforFacialRecognitioninOrthodontics... 55 ChihiroTanikawaandLeeChonho PartII MachineLearningforOralDiagnosisandTreatment 6 Machine/Deep Learning for Performing Orthodontic DiagnosesandTreatmentPlanning ....................... 69 Chihiro Tanikawa, Tomoyuki Kajiwara, Yuujin Shimizu, TakashiYamashiro,ChenhuiChu,andHajimeNagahara 7 Machine Learning in Orthodontics: A New Approach totheExtractionDecision ............................... 79 MaryLanier ZaytounBerne,Feng-ChangLin,YiLi,Tai-HsienWu, EstherChien,andChing-ChangKo 8 Machine (Deep) Learning for Characterization ofCraniofacialVariations ............................... 91 SiChen,Te-JuWu,Tai-HsienWu,MatthewPastewait,AnnaZheng, LiWang,XiaoyuWang,andChing-ChangKo ix x Contents 9 Patient-SpecificReferenceModelforPlanningOrthognathic Surgery ............................................... 105 Hannah H. Deng, Li Wang, Yi Ren, Jaime Gateno, Zhen Tang, Ken-Chung Chen, Chunfeng Lian, Steve Guofang Shen, PhilipKin ManLee,Pew-ThianYap,DinggangShen,andJamesJ.Xia PartIII MachineLearningandDentalDesigns 10 Machine (Deep) Learning forOrthodonticCAD/CAM Technologies........................................... 117 Tai-HsienWu,ChunfengLian,ChristianPiers,MatthewPastewait, LiWang,DinggangShen,andChing-ChangKo 11 AssessmentofOutcomesbyUsingMachineLearning ...... 131 Shankar Rengasamy Venugopalan, Mohammed H. Elnagar, DeeptiS.Karhade,andVeerasathpurushAllareddy PartIV MachineLearningSupportingDentalResearch 12 MachineLearninginEvidenceSynthesisResearch......... 147 AlonsoCarrasco-Labra,OliviaUrquhart,andHeikoSpallek 13 Machine Learning and Deep Learning in Genetics andGenomics ......................................... 163 DiWu,DeeptiS.Karhade,MalvikaPillai,Min-ZhiJiang,LeHuang, GangLi,HunyongCho,JeffRoach,YunLi,andKimonDivaris 14 Machine(Deep)LearningandFiniteElementModeling .... 183 Yan-TingLee,Tai-HsienWu,Mei-LingLin,andChing-ChangKo Part I Machine Learning for Dental Imaging