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DeepRadiologyWhitepaper

January2018

LastupdatedFebruary14,2018

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DISCLAIMER:ThefollowingwhitepaperismeanttodescribethecurrentlyanticipatedplansofDeepRadiology, Inc and its affiliates, (together as “DeepRadiology"), for developing a newblockchain token mechanism, (“DeepRadiology Token"), that will be used on the networksponsoredbyDeepRadiology, (“DeepRadiologyNetwork"). DeepRadiologymay from time totimerevisethisWhitePaperinanyrespectwithoutnotice.NothinginthisdocumentshouldbetreatedorreadasaguaranteeorpromiseofhowDeepRadiology'sbusiness, theNetwork,ortheTokenswilldeveloporoftheutilityorvalueoftheNetworkortheTokens.ThisWhitePaperoutlines DeepRadiology's current plans, which could change at any time at DeepRadiology’sdiscretion, and the success of which will depend on many factors outside DeepRadiology'scontrol, including market- based factors and factors within the data and cryptocurrencyindustries, among others. Any statements about future events are based solely onDeepRadiology'sanalysisoftheissuesdescribedinthisdocument.Thatanalysismayprovetobe incorrect. Thisdocumentdoesnotconstituteanofferor saleof theTokensoranyothermechanismforpurchasingtheTokens(suchas,withoutlimitation,afundholdingtheTokensorasimpleagreementforfuturetokensrelatedtotheTokens).AnyofferorsaleoftheTokensoranyrelatedinstrumentwilloccuronlybasedondefinitiveofferingdocumentsfortheTokensortheapplicableinstrument.PurchasingtheTokensoranyrelatedinstrumentissubjecttomanypotential risks. Some of these risks will be described in the offering documents. Thesedocuments, along with additional information about DeepRadiology and the Network, areavailable on our website at www.DeepRadiology.com. Purchasers of Tokens and relatedinstruments could lose all or some of the value of the funds used for their purchases. Thisdocumentisnotasecuritiesofferingorpolledinvestmentplan.ItdoesnotrequireregistrationorapprovalbytheSECoranyotherorganization.Participantsarerecommendedtoscrutinizethisdocumentandmakeprudentinvestments.

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TABLEOFCONTENTS1 ExecutiveSummary2 HealthcareToday3 ArtificialIntelligence4 TheCompany5 Team6 OurTechnology6.1 AIOverview6.2 Formalization6.3 Architecture6.4 ValidationandEvaluationMethodology6.5 BlockchainandSmartContracts7 DataandCuration8 IntellectualProperty9 OurProducts10 ClinicalTrial11 Market12 Security13 Roadmap13.1 TokenUsageRoadmap13.2 OverallRoadmap[Includinguseofventurefunds]14 Regulatory15 TokenDesign16 TokenSaleLogistics17 Disclaimers/Disclosures/Risks18 References

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1.EXECUTIVESUMMARY Modern medical imaging/radiology has revolutionized healthcare and today plays acritical role in the diagnosis and management of nearly all significant medical conditions.Unfortunately, there is a growing shortage of qualified radiologist/physicians to provideinterpretations of these studies due to increasing utilization and other factors. Theseinterpretationsalsohaveasmallbutsignificanterrorratethatcontributestohumanerrorasbeing the third leading causeof death inhealthcare after cardiovasculardisease and cancer.Finally,thecostsoftheseinterpretationsarehighandstraininghealthcarebudgetstothepointofunsustainability.Thisalsoultimatelylimitstheavailabilityoftheselifesavingtoolstothoseinneed.

2012sawthebeginningofarevolutioninartificialintelligence,inparticularusingneuralnetworksinafieldknownasdeeplearning.Thistechnologyallowedcomputersoftwareforthefirsttimetoexceedhumancapabilitiesincomplexvisualrecognitiontasks.

DeepRadiology was established shortly thereafter to apply this groundbreakingtechnologytooneoftheultimatecomplexvisualrecognitiontasks,interpretingmedicalscans.Weassembledateamofexpertsinradiologyaswellasexpertsincomputerscience.Ourteamincludes the inventor of the deep learning technology, Yann LeCun,who alsoworks as ChiefArtificialIntelligenceScientistforFacebook.RobRankin[formerCEODeutscheBankAsiaPacificRegion]alsojoinedourteamasChairmanandweraisedmillionsofdollarsinventurecapitaltomakethisdreamareality.

InNovember2017DeepRadiologyreleasedagroundbreakingreportofthefirstartificialintelligence system to interpret computed tomography [CT] scans with performance levelsgreater than thatofhumanradiologists.Thesystemwasdevelopedand trainedusingover9millionCTscanimagesofthehead.Ithasthefollowingadvantagesoverhumanradiologists:

-ThetimeforaradiologisttointerpretaCTscanoftheheadisapproximately3to4minutes.Oursoftwarecandoitinafractionofasecond.

-Theerrorrateforoursystemislowerthantheerrorratesforhumanradiologists.

-Thecostforradiologistinterpretationisapproximately$50[U.S.rates].Ourcostis$00.002.

WearedevelopingsoftwaretointerpretothermajormedicalCTscantypesaswellas

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formagneticresonanceimaging[MRI],plainx-rays,ultrasound,mammographyandnuclearmedicine.OurproductsarenowbeginningtobedeployedtohospitalsandotherimagingfacilitiesintheUnitedStates.

DeepRadiologyisalsonowincorporatingblockchaintechnologyusingsmartcontractsandutilitytokenstoallowfurtherefficienciesinourserviceatscalewithgreatersecurityandreliability.Savingswillaccrueaswedisintermediateourprocessesandpassthesavingsontoourcustomersandothercommunitymembers.Weseedirectbenefitsinfourareas:

-Eliminationofexcessiveexchangefeesandtariffsforpaymentsinforeigncurrency.

-Eliminationofaccounting,billing,paymentprocessing,andcollectionfees.

-GreaterflexibilityinrewardstoincentivizeourDeepRadiologycommunity.

-Decentralizationofresourcesusingourcommunityforgreatersecurityandreliability,furthercostsavings,andallowcommunitymemberstoshareinrevenuefromourservices.

DeepRadiologycombinestwoofthemosttransformativetechnologiesofthe21stcentury,deeplearningartificialintelligenceandblockchainwithsmartcontractstoallowlifesavingmedicalimagingtechnologytobedeliveredtomanymorewhoneeditatlowercostworldwide.

Wehopethatyouwilljoinusonourjourneytochangetheworld!

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2.HEALTHCARETODAY Modernmedicalimaging/radiologyhasrevolutionizedhealthcareandtodayplaysacriticalroleinthediagnosisandmanagementofnearlyallsignificantmedicalconditions. The usage of medical imaging services has increased with increasing complexity ofimaging studies despite variousmeasures attempting to control utilization. It is expected tocontinue to rise given the aging population in the U.S. andworldwide. Demand for imagingserviceshasalsorisenasdevelopingcountriesincreaseutilization. This contributes to a growing shortage of qualified radiologist physicians to provideinterpretationofthesestudies.Thesephysiciansareexpensivetoproduce,takingmanyyearstotrainandfollowingtraining,mostphysicianswillonlybecontributingtotheworkforceatfullcapacityforsome20to30yearsandusuallythenreplaced. Evenwiththebesttrainingandexperience,thesephysiciansdohaveaconsistenterrorratewhen they interpretmedical imaging scans. This small but significant error rate in theinterpretation of these studies contributes to the fact that human error is the third leadingcauseofdeathinhealthcareaftercardiovasculardiseaseandcancer[48].

Figure1-Globalhealthcareexpenditure-Ref:Deloitte[34] Finally, thecosts for theseandsimilar servicesare increasingandstraininghealthcarebudgetstothepointofunsustainability[Figure1].Thisalsoultimatelylimitstheavailabilityoftheselifesavingtoolstothoseinneed.

Medical imaging is amajor costwithinhealthcare, accounting forover10%ofoverallhealthcareservicesglobally.Healthcarecost isalarminglyunsustainablebutcontinuestorise.The U.S. is amajor spender of healthcare. In fact, the recent U.S. Center forMedicare andMedicaidServices(CMS)reportshowsthatU.S.HealthCareExpendituregrewat5.8%in2015

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toUSD3.2trillionandaccountedfor17.8%ofU.S.GrossNationalProducts(GDP).IntheU.S.federalbudgetof2017,healthcaretoppedallspendingandalongwiththat

medical imaging cost is expected to rise further again. Containing and reducing costs arefundamentalgoalsandmajorinterestsofallpartiesdealingwithhealthcare.Theseconcernscannowbegintobeaddressed-throughtheuseofartificial intelligenceandblockchaintechnology.

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3.ARTIFICIALINTELLIGENCE 2012sawthebeginningofanewrevolutioninartificialintelligence,inparticular,afieldknownasdeep learning. This technologyallowed for the first time for computer software tolearnfromdataincertainvisualrecognitiontasksthatresultedinperformancethatexceededhuman capabilities. This report was a breakthrough that used deep convolutional neuralnetworks to almost halve the error rate for object recognition, and precipitated the rapidadoptionofdeeplearningbythecomputervisioncommunity[30].Thiswasmadepossiblebyprogressinthealgorithmsused,aswellasadvancesinthecapabilitiesofcomputerhardware[largelygraphicalprocessingunits]forefficientlyperforminglargequantitiesofcertaintypesofcomputationsneededbythealgorithms[Figure2].

Figure2.Multilayerneuralnetworks Deeplearningallowscomputationalmodelsthatarecomposedofmultipleprocessing

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layers to learn representations of data withmultiple levels of abstraction. Thesemethodshave dramatically improved the state-of-the-art in speech recognition, visual objectrecognition,objectdetectionandmanyotherdomainssuchasdrugdiscoveryandgenomics. Deep learning discovers intricate structure in large data sets by using thebackpropagationalgorithmtoindicatehowamachineshouldchangeitsinternalparametersthat are used to compute the representation in each layer from the representation in theprevious layer [Figure 3]. Deep convolutional neural networks have brought aboutbreakthroughs in processing images, video, speech and audio, whereas another form ofneuralnetworkknownasrecurrentnetshaveshedlightonsequentialdatasuchastextandspeech[Figure4].

Figure3.Insideaconvolutionalneuralnetwork Akeyarticleon“DeepLearning” in the journalNatureco-authoredbyDeepRadiologyleading team member Yann LeCun (also Facebook AI Director), Geoffrey Hinton (Google AIDirector) and Yoshua Bengio (Microsoft Advisor) gives fundamentals in the understanding ofdeeplearningandhowitnowworksinapplyingartificialintelligencetodifferentfields[31].

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Figure4.Fromimagetotext DeepRadiologycombinestheseadvanceswithauniqueteamof leadingexperts inthefieldwith domain expertise at the highest levels in both artificial intelligence/deep learning/neuralnetworksandmedical imaging.DeepRadiologyteamalsohasaccesstounprecedentedamounts of high quality optimally labeledmedical datawithwhich to produce our softwaresystems.

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4-THECOMPANY DeepRadiologywasformedinDelawareasacorporationinJuly2015[47].TheoriginalgoalofDeepRadiologywastoexplorethecapabilitiesofartificialintelligence/deeplearningtoaidinthemedicalimagingscaninterpretationprocess.Overthefirsttwoyearsofoperationweassembled a team of physician/radiologists and experts in computer science and weresuccessfulincreatingrevolutionarydeeplearningsoftwaretoaccomplishthis. Venture Capitalists have already reviewed and validated the business model ofDeepRadiology. Thecompanysuccessfullyraisedmillionsofdollars inventurecapitaltofundfurtherproductionofproductsandinitialdeploymentoftheseservices.Ourteamincludestheinventor of the deep learning technology, Yann LeCun, who also works as Chief ArtificialIntelligence Scientist for Facebook. Robert Rankin [former CEO Deutsche Bank Asia PacificRegion] joined our team as Chairman as we begin to deploy our services in the U.S. andinternationally. We now are using blockchain/smart contract technology to deploy utility tokens tofacilitateinternationaltransactionsandmicropaymentsaswellasincentivizingandgrowingourDeepRadiology developer and user community.We also see blockchain/smart contracts anddistributedarchitecturesasawaytofurtherreducecosts,increasesecurity,andreliabilityandreturnrevenuetoourcommunityinsteadofpayinglegacyserviceproviders. The official address for DeepRadiology is 2461 Santa Monica Blvd, Suite 105, SantaMonica, California 90404 which is a mailing address. For security reasons, due to theconfidentialnatureofthemassiveamountsofmedicaldatathatwehouse,wedonotacceptvisitorstoouractualofficesexceptbyappointment.Ourproductsarebeingusedinservicingmanyhospitalsfromwhichwereceivefeedbackforfurtheroptimizationanddevelopment.Wehavepayingcustomersandexpectincreasingrevenuein2018.Allourservicestoclientsareconfidential. DeepRadiologycombinestwoofthemosttransformativetechnologiesofthe21stcentury,deeplearningartificialintelligencecombinedwithblockchain,smartcontractsandutilitytokenstoallowlifesavingmedicalimagingtechnologytobedeliveredtomanymorewhoneeditatlowercostworldwide.

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5-THETEAM DeepRadiology has brought together a team of deep learning foremost expertsincluding the ‘inventor of Deep Learning’, Yann LeCun and other hands-on nationally andinternationallywell-knownandwell-respectedArtificial IntelligenceProfessors, in-houseDeepLearning/Machine Learning/Blockchain PhD engineers, Software Development and Networksupportengineers,PhysicianRadiologistexpertsandFDAregulatoryexperts.

KimNguyen,MDChiefExecutiveOfficerandCo-Founder Prior to co-founding DeepRadiology, Kimwas Assistant Professor of Radiology at TheUniversityofCalifornia,LosAngeles(UCLA)SchoolofMedicineandwasafoundingmemberofalargeteleradiologycompanythatlaterhadaninitialpublicoffering(IPO)ontheNASDAQ.Asuccessful serial entrepreneur, Kim then started a large and successful U.S. nationwidetelemedicine company. In addition to being an expert radiologist, Kim is also highlyknowledgeablewiththemanagementofbigdata inmedical imaging.Kimhasmorethantwodecadesofexperienceinbuildingcost-effectiveinternationalmedicalnetworkingsystemswithsecurityandhighlevelreliabilityforglobalclinicalhospitaloperations. AVietnamwarrefugee,Kimdriftedatseainasmallboatforhalfamonthwithoutfoodorwater. Kimwas fortunately rescued andbrought toAustraliawhere Kim restarted life at 14.Fouryearslater,Kimenteredmedicalschoolandcompletedmedicalschoolat23withhonors.KimcametotheU.S.undertheScientistwithExtraordinaryAbilityprogramwiththeUniversityofCaliforniasystem.Kimhaswrittenmanyscientificpapersandtextbooks.Kim’searlierworkwasonfundamentalradiologicalsciencesonMRI,particularlyonMRcompatibleinterventionaldevices,braincancerandAlzheimer’sdisease.

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RobertLufkin,MDCo-FounderandPrincipalMedicalArchitect Priortoco-foundingDeepRadiology,RobwasaProfessorofRadiologyatUCLASchoolofMedicine.RobhasalifelonginterestinartificialintelligencewithundergraduatespecializationincomputerscienceatBrownUniversity.Internationallyknownforscientificworksinradiology,Robhaswritten14textbooks,over300scientificpapersandhasbeenaninvitedspeakerin18countries. His honors include serving as President of the Society of Magnetic ResonanceImaging,PresidentoftheAmericanSocietyofHeadandNeckRadiology,andnumerousotherprofessionalaffiliations.'Beingabletoemployblockchainandartificialintelligencetodeveloprevolutionarynewmedicaltechnologytoimprovehealthcareisadreamcometrue.'

YannLeCun,PhDArtificialIntelligenceStrategist Yannbringstrulyuniquetechnicalexpertisetoourteam.YanniswidelyregardedastheinventorofdeeplearningandinparticulardeepconvolutionalneuralnetworksthatmadetheDeepRadiologytechnologicalbreakthroughspossible.Tobeclear,Yann'sroleisasanadvisor.InadditiontohisworkwithDeepRadiology,YannservesastheChiefArtificialIntelligenceScientistforFacebook.YannisalsoProfessorofComputerScienceatNewYorkUniversity.

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RobertRankinBEC,LLBChairman,DeepRadiology We are extremely fortunate to have Rob as Chairman of DeepRadiology. Rob bringsyearsof internationalbankingand financial expertise to the team. Rob'spreviouspositionsincluded serving as CEO of Deutsche Bank, Asia Pacific Region, Head of Corporate Finance,DeutscheBank,andHeadofInvestmentBanking,UBSAsia.

StefanoSoatto,PhDDeepRadiologyArchitect Stefanoisoneoftherecognizedworldexpertsincomputervision.StefanoisalsoDirectorofMachineLearningatAmazonWebServices.Stefano’sothertitlesincludeProfessorofComputerScience,UCLAandDirectoroftheUCLAComputerVisionLaboratory.

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AndreaVedaldi,PhDDeepRadiologyArchitect FewindividualshavethereputationforexcellencethatAndreahasforhisworkinthefieldofdeeplearningandAndreabringsuniquetalentstotheDeepRadiologyteam.AndreaisalsoAssociateProfessorofEngineeringatOxfordUniversity.

ZhuowenTu,PhDDeepRadiologyArchitect Zhuowenissecondtononeinhisexpertiseintheapplicationofdeeplearningtomedicalimaging.ZhuowenisalsoAssociateProfessorofComputerScience,UCSanDiego(UCSD).

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JamesonMerkow,PhDMasterTechnologyArchitect Jameson is known for his expertise in three dimensional (3D) convolutional neuralnetworkswhichcanbeveryusefulinmedicalimaging.JamesonplaysakeyroleintheoverallartificialintelligencestrategyofDeepRadiology. TheDeepRadiologyteamalsoincludesover30medicaldoctorswithboardcertificationin radiology, large team of in-house Deep Learning/Machine Learning/Blockchain/SmartContract PhD engineers, Software Development and Network support engineers, and FDAregulatory experts. We have many younger energetic innovative team members who workcloselytotacklethecomplexityofthechallengingtasksweareaddressing.

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6-OURTECHNOLOGY6.1AIOVERVIEW AlthoughoursystemleveragesonrecentdevelopmentsinDeepLearning,standard,off-the-shelf systems are insufficient for our needs to address our challenge. Hence, we makethreekeycontributions: First, we train our neural networks from millions of medical images professionallyannotated, thus distilling them from the observation of thousands of hours of human labor.Second,weredesignstateof-the-artneuralnetworkarchitecturestobettermatchthestatisticsofthoseimages,whichdiffersubstantiallyfromthenatural(everyday)imagesforwhichtypicalarchitecturesareoptimized.Third,wecarefullyevaluatethereliabilityoftheresultingsystemand show that it can be used to identify, automatically, a large fraction of pathology withcomparableorbetteroverallaccuracythanexpertradiologists. OneattractiveaspectofusingMachineLearningtodevelopdiagnosticsystemsisthat,while thehumanvisual systemhasevolvedovermillionsof years tobe attuned to interpretnatural images, it is not naturally suited to interpret medical images. This is why trainingradiologistsisalongprocess,andthemappingfromnon-opticalsensorysignals,asincomputedtomography[CT]ormagneticresonance(MR),toimagesthatcanbeviewedbyahumanmayentailinformationloss. Thismappingisnotnecessaryforanautomatedsystem,thatcanbetrainedtoperforminferencedirectlyfromrawsensoryinput,withouttheneedforopticalvisualization.Thisoffersthepotentialforpreclinicaldiagnosis,beforediseaseismanifest inanoptical imagerenderedtoaradiologist. Thesebenefitscanonlymaterialize ifneuralnetworkscanbetrainedfromasufficientquantity of high-quality data, the acquisition of which is often one of the most significantpracticalhurdles.AtDeepRadiology,weleveragea largecuratedcollectionof imagingstudiestotrainadeepneuralnetwork,whichisaparametricclassoffunctionswhoseparameterscanbeadaptedtofitacomplexmapfromimagingdatatoclassificationoutcomesmatchingthatofexpertradiologists.Detectioncomprisesabinaryclassificationtask,astowhetherapathologyispresent,amulti-classclassification(whichofasetofpathologies),andlocalization(whereinthevolumeisthepathologymanifest). WithinMachineLearning,theuseofdeepneuralnetworksforinterpretingimagingdataresurged toprominence in2013 [16],despite thekey toolsbeingavailable since longbefore[22], following the availability of large annotated datasets of natural images [3] as well as

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computinghardwareinitiallydevelopedforgraphicsrendering.Ourinitialattempt,in2014,toexploitanetworkpre-trainedonImageNetdataandfine-tunedtoarelativelysmallnumberofimaging studies gave encouraging but far from human-level results due to the significantlydifferentphenomenology. Forexample,naturalimageclassificationisunaffectedbychangesofintensityvaluesolongaslocalorderingisnotaffected(contrastchanges),whereastheintensityvaluerecordedat a pixel of a CT scan, measured in Hounsfield units, is informative of certain classes ofpathology. Humans cannot perceive absolute luminance, and their perception is largelycontrast-invariant. Similarly, natural images are subject to visibility artifacts (occlusions),whereasmedical imaging sensors aredesignedprecisely toovercomeocclusion. Large shapevariationsinducedintheimagedomainbychangesofvantagepointinanaturalimagedonotchange the identity of the object being portrayed, whereas deformation of anatomicalstructuresinamedicalimageareoftenindicativeofpathologies.Insomerespect,therefore,medicalimagesaresimplerthannaturalimages,asthemostdetrimental sources of nuisance variability (viewpoint, illumination and partial occlusion) areabsent.Ontheotherhand,theyarechallenginginthatsubtleclass-specificvariationsareoftenobfuscatedbysignificantintra-individualvariability. Whereasmuch of the effort in training classifiers for natural images goes to discardnuisance variability, most of the effort in training deep neural networks, and specificallyconvolutional ones (CNNs), goes to disentangling subtle class-specific variability from largeintra-individual nuisance variability. The practical consequence of this is that simplydownloading a pre-trained network and hoping that fine-tuning it on a small number ofannotated medical images will achieve satisfactory performance is wishful thinking, andtrainingfromscratchinamodality-specificmannerisnecessary. Relatedwork exploiting deep neural networks inmedical imaging includes [6],wheredermatology images are automatically evaluated by the Inception V3 network trained andevaluated using nine-fold cross validation on a set of 129,450 images. The challenges indermatologyaredifferent than in radiology images, andmoreakin tonatural images,wherethere is irradiancevariabilityduetothe interplayofthereflectanceanddiffusionpropertyofthetissueswiththepropertiesoftheilluminant.Forexample,inCT,theprobingsignalisnotunstructuredelectromagneticinthevisiblespectrum,butratherpenetratingradiationintheX-ray band, that is undeflected by the tissues; furthermore, the data is volumetric, and thephenomenology is substantially different, measuring absorption (not subject to occlusion),ratherthanreflectance. Work on X-ray includes the recently disclosed CheXNet, that is claimed to reachradiologist-levelpneumoniadetectiononchestx-rayswithDeepLearning[20].Thevalidationsetthere is limitedto420 images, inourviewinsufficienttodeterminesuitableperformancewithasufficientlevelofconfidence.Ascomparison,wetestoursystemsonvalidationsetsofmillionsofimages.

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Additionalmodalitieswheredeeplearninghasbeendeployedincludefundusimagingtoassess diabetic retinopathy [8], electrocardiogram for arrhythmia detection [19], andhemorrhage identification [7]. Automated diagnosis from chest radiographs has receivedincreasing attention with algorithms for pulmonary tuberculosis classification [17] and lungnoduledetection[11].[12]studiedtheperformanceofvariousconvolutionalarchitecturesonmultiple datasets. Recently, [26] released a new large scale dataset ChestX-ray-14, withperformance benchmarked using ImageNet pre-trained architectures. Competition on thisdatasethasalreadybegunwithmultipleworksshowingimprovedperformance[29,20]. Ingeneral,theuseofdeeplearningformedicalimaginghasbeenthesubjectofintenseinterest, includingdedicatedsessionsatmedical imagingconferences,andbookpublications.Thisisunderstandable,butthedevilisinthedetail,andwefindthequalityofthedata,aswellasitscurationincludingtheontologyofclassestobetrainedfor,tobeascriticalasthechoiceofarchitectureoroptimizationscheme.

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6.2FORMALIZATION Ifwecallx={xi,i=1,...,N}asetofimagesinastudy,withxi∈[0,1,2,...,L−1]m×nanimagewithLgrayscalevalues,andm×npixels,andxjoneofj=1,...,Mstudies,andy∈{0, 1, . . . , K} one of K classes, each representing a disease or a specific phenomenologicalaspect of the study that might be of interest to a radiologist, then we are interested inconstructing a classifier, f : X → Y ; x 7→ y that, in response to an input image or study,producesalabely.Moreinparticular,weareinterestedinthisfunctiontobe(notnecessarilyuniquely) determinedby a set of parametersw∈ R P ,whereP caneasily be in the tensofmillion. Furthermore, we want f to be written as a simple and constant function (called aclassifier)ofamorecomplexfunction(calleddiscriminant,orembedding,orrepresentation)φ:X×W → R K + ; (x, w) 7→ φw(x) where φw(x)[k] denotes the k-th component of the K-dimensional vector φw(x). Assuming that medical images x and their associated label y aredrawn from an unknown probability distribution P(x, y), the optimal (Bayesian) discriminantwouldbethefunction φw(x)[k]=P(y=k|x)(1)wheretheright-handsideistheposteriorprobabilityofthelabelgiventheimagex.Fromnowon we do not distinguish between images and studies, which is an application-dependentchoice. In this work, we choose the discriminant φw(·) among the class of functionsrepresented by deep (multilayer) neural networks. These are universal approximants [2],meaning that, given sufficient parameters, they can approximate any finite-complexityfunction.Machinelearning-theoreticconsiderationsarebeyondthescopeofthispaper,wherewesimplyassumethattheoptimaldiscriminantiswithinthechosenfunctionclass. Iftheoptimaldiscriminantisavailable,inferenceproceedssimplybycomputingthemaximumoverallk∈{0,...,K}: f(x)=argmaxkφw(x)[k].(2)The goal of learning is, given samples from the joint distribution P(x, y), to determine theparameterswsothat,onaverage,theerrormadeinapproximatingf(x)withtheright-handsideof (2) is smallest. The crux of thematter is thatwe cannot compute the average (expectedvalue)with respect to P(x, y) sincewe do not have access to it, so it is standard practice inMachineLearningtominimizethesampleaverage(empiricalloss), wˆ=argminwX(xi,yi)∼P(x,y)`w(xi,yi)(3)where `(x, y) is the loss incurredwhen rendering thedecision y in response to the image x.Relatingtheempiricallosstotheexpectedlossrequiressomeassumptionsonthedistribution;

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studyingthisrelation isthemainsubjectofstatistical learningtheory,whichwedonotdelveintohere.Sufficeforustosaythatsomeformofregularizationistypicallynecessarytoensurethat the minimizer of the empirical loss bears some resemblance to the minimizer of theexpected loss, and therefore can generalize to unseen samples [25]. In the case of trainingconvolutional neural networks (CNNs) using stochastic gradient descent (SGD), suchregularization takes many forms, some implicit in the nature of SGD, some explicit (e.g.,Dropout[23]),othersrooted inthechoiceofarchitecture(e.g.,pooling).This isallcustomaryandwereferthereadertoanytextbookinmachinelearningfordetails. Thechoiceof loss` isspecifictothetaskof interest.Formulti-classclassification, it iscustomary to use (average) empirical cross-entropy, represented using the assumptionsoutlinedabove,as `(xi,yi)=−logφw(xi)[yi].(4) Theminimizerof(average)empiricalcross-entropycanbeshowntobetheminimizeroftheaverageprobabilityoferror inastandardzero-onelosswhereeveryerrorhasequalcost.Thisisnotthecaseinmedicalimageinterpretation. Crucial to Medical Imaging is the strong asymmetry between type-one errors (falsealarms) that can result inunnecessary treatment and increased8 costof care, and type-twoerrors(misseddetection)thatcanbefatal.Thismustbetakenintoaccountinthecomputationoftheaverageloss,orrisk,whichhastoweigheacherrorbyitscost.Anotherasymmetry isdueto the incidenceofdisease:Becausepathologyare thankfully rareamong the set of all studies conducted, a trivial classifier that always declares absence ofdiseaseswould achieve seemingly reasonable error rates. Of course, chance level is definedrelativetothestandardincidenceofdisease,andthisisagainapointofdepartureforMedicalImaging compared to standardnatural image classification in the contextof image searchorcontent-based retrieval. Finally, the K classes we consider are not necessarily mutuallyexclusive,andinsomecasestherearestrongdependencies,soonebeingmanifestaffectstheprobabilityofothersbeingtoo.

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Figure5.IllustrationofhierarchallossusedtotrainDeepRadiologyNet. This is taken intoaccount inDeepRadiologyNetbyemployingaknowledgegraph thatincorporates domain expertise from professional radiologists, using a hierarchical loss thatpenalizesdifferentclassesdifferentlyandaccounts for lackofmutualexclusivity,andvariousbalancing techniques to account for the prior distribution of diseases expected in thepopulation[Figure5]. Inourhierarchicallossfunction,pathologiesaregroupedaccordingtomultiplecriteria,includingpathologylocation,clinicalsignificanceandpathologytype.Asimpleexamplegroupspathologies based solely on their clinical significance; one possible grouping could be zeropatient-risk,moderatepatientriskandimmediatewithhighrisktothepatient.Inthisscenario,shouldan imagehavea lowormoderaterisk labelaswellasahighrisk label, the lower risklabel is ignored in favor of the potentially life-threatening pathology. Another possible losshierarchicallygroupspathologiesbasedontheirtypeand/orlocation.Forexample,intracranialhemorrhage(ICH)couldbeonesuchgroupingwhich iscomposedofpathologies likeepiduralhematoma, subdural hematoma, subarachnoidhemorrhage, intraventricularhemorrhageandparenchymalhemorrhage. AconsiderableamountofeffortinthedesignofDeepRadiologyNetis,inadditiontothechoiceofarchitecturesandlearningmachinerydescribedinthenextsection,inthecurationofdataandtheirmanagementtoensurethatpopulationanddiseasepriorsaretakenintoaccountwhen specifying the compositionof specialistnetworks inDeepRadiologyNet in a statisticallycorrectmanner,whilesatisfyingknowndependenciesfromdomainexpertise.

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6.3SYSTEMARCHITECTURE In the next section we describe the choice of architecture, loss function, andoptimization, and in the following one we describe the methodology for data curation andevaluation. Our network is composed of an ensemble of multiple GoogleNet-like networks[24]. Figure 6 shows a comparison between the architecture used in DeepRadiolgyNet andotherarchitecturechoices:DenseNet[10],ResNet[9]andResNeXt[28].Thesenetworksweretrained on the same data as DeepRadiologyNet, and the comparison was carried out onseparatevalidationsetofover9000studies.

Figure6.Receiveroperatingcurvesmeasuringperformanceondetectionofclinicallysignificanttraits of popular architectures [10, 9, 28] and the architecture used in DeepRadiologyNet.Validationwascarriedoutonasetof9000studies. Each network in DeepRadiologyNet was trained starting from a different randominitializationandtraversethetrainingdatainadifferentrandomizedorder.DeepRadiologyNetcontainsnetworksthatweretrainedusingvariousbaselosstypesincludinghingelossandsoft-max with multinomial logistic loss and may use different hierarchal loss mappings. Trainingupdates were carried out using Adam optimization [15] or stochastic gradient descent withmomentum.Trainingimageswereaugmented,on-the-fly,byrandomlyrotating,rescaling,andmirroring imagesby clinically appropriate values.Networkswere regularized through varyingdegrees of dropout and early stoppingwhile training for a fixed number epochswith a steplearningratepolicy.

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6.4VALIDATIONANDEVALUATIONMETHODOLOGY Wemeasure the performance of DeepRadiologyNetwithmultiplemetrics and labels,recording clinically insignificant errors, clinically significant errors, and validation diagnosticsdesignedtobeinterpretablebyhumanexperts. Weproducereceiveroperatorcharacteristics(ROC)curvesandclassifiererrorratestoenablemodulatingdetectionperformancewithfunctionalusefulness:Itiseasytoachievehighprecisionattheexpenseoflowrecallandvice-versa.Carefullymodulatingtheoperatingpointiscriticalfortheviabilityofanautomatedorassistedinterpretationservice. Thechoiceofoperatingpointdependsonacceptablelevelsofcertaintywhichishighlypathology-dependent, application-dependent, and a complex issue not discussed in thismanuscript.Here,welimittoshowingourperformancecurves,togivethereaderfullaccesstothetradespace.Confidenceintervals(95%)ofourROCcurvesarecomputedthroughbootstrapre-sampling.

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6.5BLOCKCHAINANDSMARTCONTRACTS SatoshiNakamoto revealed theconceptofBlockchainandBitcoin in2008 [35],whichwasbuiltonpreviouspublicationsandinnovations,likeAdamBack’sHashcash[36],WeiDai’sB-money [37]andNickSzabo’sBitGold [38].Thisdisruptive ideasolvedthedoublespendingproblemwith a distributed ledger using Proof-of-Work and “building on the longest chain ”consensus.NickSzaboalsointroduced“smartcontracts”in1996[39],inapaperthatdescribeshowtofacilitate,verifyandenforcecontractsindigitalsettings.Creatingtheproperincentivesarekeyinanysustainablebusinesssituation[40].Cryptoassetsandsmartcontractsnowmakeitpossible tocreateveryefficient incentive systemsand tokeneconomies,whichcanquicklysurpasstraditionalplayers. The internet is in themiddleof anew revolution: centralizedproprietary servicesarebeing replaced with decentralized open ones; trusted parties replaced with verifiablecomputation; brittle location addresses replacedwith resilient content addresses; inefficientmonolithic services replaced with peer-to-peer algorithmic markets. Bitcoin, Ethereum, andotherblockchainnetworkshaveproventheutilityofdecentralizedtransaction ledgers.Thesepublic ledgers process sophisticated smart contract applications and transact crypto-assetsworthtensofbillionsofdollars[44]. These systems are the first instances of internet-wide Open Services, whereparticipants form a decentralized network providing useful services for pay, with no centralmanagementortrustedparties.Forexample,theInterPlanetaryFIleSystem[IPFS]hasproventheutilityof content-addressingbydecentralizing theweb itself, servingbillionsof filesusedacross a global peer-to-peer network[45]. It liberates data from silos, survives networkpartitions, works online, routes around censorship, and gives permanence to digitalinformation. Bitshares and Etherdelta are examples of decentralized exchanges and Steemitshowsthepossibilitiesofadecentralizedsocialnetwork[53-55].DeepRadiology is now incorporatingblockchain technologyusing smart contracts andDRADutilitytokenstoallowfurtherefficienciesinourserviceatscalewithgreatersecurityandreliability.Savingswillaccrueaswedisintermediateourprocessesandpassthesavingsontoourcustomersandothercommunitymembers.Weseedirectbenefitsinfourareas: -Reductionofexcessiveexchangefeesandtariffsforpaymentsinforeigncurrency. -Reductioninaccounting,paymentprocessing,andcollectionfees. -GreaterflexibilityinrewardstoincentivizeourDeepRadiologycommunity.

-DecentralizationofourGPU[graphicalprocessingunit]processingresourcesusingour

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communityforgreatersecurityandreliabilityandallowcommunitymemberstoshareinrevenuefromourservices

First,paymentinforeigncurrenciesincursadditionalchargesduetobankingexchangefeesandothertariffs.Byacceptingpaymentsintheformofourutilitytokenswecanremovetheexpenseoftheseintermediaries.Wewillofferthisoptiontoourforeigncustomerswhowillhave discounted pricing for payments using the utility tokens. This will have the effect ofpassingonthesavingsduetotheseefficienciestoourcustomersaswellasmaintainingvaluefortheutilitytokens. Secondly,innearlyallhealthcareservices,thereisconsiderableoverheadinaccounting,billing,paymentprocessing.Byacceptingpaymentsintheformofutilitytokenswhichwillthenbe processed using smart contracts to automatically release the services and performaccounting,we can disintermediate these other providers and further lower the cost of ourservices.Thiswillbeprovidedasalowercostoptionforourcustomerswhochoosetousetheutilitytokens.As intheexampleabovethiswillalsohavetheeffectofpassingonthesavingsdueto theseefficiencies toourcustomersaswellas tending tomaintainvalue for theutilitytokens[Figure7].

Figure7.Lowerchargesforservicespaidforintokensversusfiatcurrency. DeepRadiology has a large and growing community/ecosystem. This diverse groupconsists of medical imaging professionals, other healthcare professionals, patients, artificialintelligence/deep learning professionals, blockchain/cryptocurrency professionals, and anyinterestedindividualswhojustlovethetechnology[Figure8].Thiscanbeintheformofa'bugbounty' fordeveloperswho identify issueswith software thatneed tobe correctedorothermembers who give us reviews and feedback on other aspects of the community. We alsoanticipate rewarding upvoted contributions as well as seeking community guidance onprioritizingnewdirections fordevelopmentand productsandservices.Byutilizingourutilitytokenfortheserewardswecaneliminatethecostsofhavingtopayintermediariesforbankingservices,wires,andexchangesinthecaseofforeigncurrency.Inadditiontheutilitytokensgiveusmuchgreaterflexibilityinpaymentamountsbyallowingmicropayments.

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Figure8DeepRadiologyCommunityComponents Thedeepconvolutionalneuralnetworksthatareatthecoreoftheartificialintelligencetechnology that drives DeepRadiology require massive amounts of processing power that isprovidedbygraphicalprocessingunits[GPUs].Agreatdealoftheexpenseofdevelopingandtrainingtheneuralnetworksaswellasrunningthenetworksonpatientimagestoprovidetheserviceisrelatedtothis.Thecurrentmodelistohavetheprocessingperformedcentrallywithlarge providers such as Amazon Web Services, Google Cloud, Microsoft Azure, or similarprovidersaswellasinhouse. Anysuchcentralmodelhascertainvulnerabilitiesbasedonthecentralizeddesign.Anycentralized system has security issues due to hacking. There are numerous examples of thisproblemwithlargesupposedlysecurecentralsystems.TheEquifaxbreechexposedthedataof143millionAmericanstohackers[33].AnotherbreechatYahoocompromisedthedataof500millionusersworldwide[43]. This hacking problem is especially significant for healthcare records. HIPAA (HealthInsurancePortabilityandAccountabilityActof1996),isaUnitedStateslegislationthatprovidesdata privacy and security provisions for safeguarding medical information, particularly inhospitalsandclinics’ElectronicMedicalRecords(EMR).TraditionalEMRsystems,despitebeingHIPAA-compliant,arenoexceptiontosuchexposure. 98percentofcompromisedhealthcarerecordsareduetohacking[46].Wede-identify/anonymizeourpatientrecordstolimitthisbutthepointisacentralsystemisvulnerable.

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A second vulnerability with any sort of centralized system is reliability. This occursbecauseofthefactthatthemorecentralizedtheserviceit,thelessredundancyispresent. By creating a blockchain based decentralized FOG processing networkmany of thesevulnerabilities of the central systemwould bemitigated [Figure 9]. A distributedblockchainbasedsystemwouldbemoredifficulttohackthanacentralizedversion.Thedistributednaturewouldalsodramaticallyincreasethereliabilityofthesystemasitwouldnolongerhaveasinglepoint of failure. And the payments that previouslywent to the legacy processing providerscould now be paid out our own community members who would choose to share theirprocessingpower.Asitturnsout,thesameGPUsthatcanbeusedforcalculatingcryptographichashes to mine Ether or other cryptocurrencies can also be used for this neural networkprocessing.

Figure9.CloudversusFOGcomputingarchitecturesSomecurrentblockchainbaseddistributedcomputingprojectsareunderdevelopmenthowevertheyarenotidealforourapplication.Inordertobeoptimallyefficientallprojectsbuildouttowardstheirusecase.GOLEM,SOMN,andsimilarsystemsareambitiousandrevolutionaryprojectsbuttheirstatedgoalistobuildadistributedgeneralpurposeFOGcomputingplatform[49,50].Whiletheprocessingthatwedocanbedoneonageneralpurposecomputer,processingfordeeplearningofmedicalimagesbyoursystemhasuniqueperformancerequirementsthatarenotoptimizedinageneralsystem.ForoptimalperformanceourusecasehasverydifferentrequirementsthanthoseofGOLEM,SOMN,orsimilarprojects.Forexample,everyiterationofatypicalneuralnetforwardpasstakesexactlythesame

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amountofFLOPS.ThereiszerovariabilitybasedonthedifferentexecutionpathsourcodetakesthroughanysprawlingC++codebase.Ofcourse,onecouldhavedynamiccomputegraphsbuttheexecutionflowisnormallystillsignificantlyconstrained.Thiswaywearealsoalmostguaranteedtoneverfindourselvesinunintendedinfiniteloops[51].Wealsohavetightlycontrolledmemoryuse.Relatedtotheabove,thereisnodynamicallyallocatedmemoryanywheresothereisalsolittlepossibilityofswappingtodisk,ormemoryleaksthatyouhavetohuntdowninyourcode.Finallysincewewillbehandlingsensitivemedicalinformationtherearealsononnegotiablesecurityandprivacyrequirementsthatwewillbeabletobuildintoourownsystemratherthantrustingthirdpartiesthatmaynotprioritizethatrequirementashighlyaswehaveto.Forthesereasons,wehavechosentocreateourownpurposebuiltFOGsystemwiththeusecaseandspecificrequirementsthatwillbemostefficientandsecureforourmedicalimagingdeeplearningapplication.ThusbydecentralizingourGPU[graphicalprocessingunit]processingresourcesweobtaingreatersecurityandreliabilityandatthesametimeallowcommunitymemberstoshareinrevenuefromourservices.

Figure 10. Dramatic cost savings with distributed semiautonomous[DSO] or autonomousorganizations[DAO] The next step in our plan beyond efficiencies of tokenized business transactions,migration to FOG computing[ to share revenue with our community and improve

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reliability/security] is tomove to fully autonomousor semiautonomousorganizations forourproductsthataresufficientlymaturetoruninthatmode.ForexamplewhenaCThead interpretation system is sufficientlydevelopedand robust,onecould imagine a scenario where it would be 'released' to run in such a mode and realizetremendouscostsavingstothecustomeraswellasotheradvantages[Figure10].Traditionalcorporatestructureisdesignedtouseemployeestoextractmaximumvaluefrom the customers for the benefit of the shareholders. This results in fundamentallymisaligned interests. This structure is particularly problematic in the situation of healthcarewheretheultimategoaloftheorganizationisnottobenefitthecustomers/patientsbutrathertheshareholders.Adistributedautonomousorsemiautonomousorganization/communityhasthe advantage that the opposed interests of the shareholders, employees, and customerspresentinlegacycorporatestructurearenowallalignedintheformoracommunitymemberwhoisastakeholder/shareholderaswellasacustomer/patientandmayalsocontributetotheorganizationasanemployee[Figure11].

Figure11.RealignmentofinterestsinDAOversusCorporationThe futureofmaximallyefficientbusinesseswill inmanycasesbe inusingadistributeddecentralized autonomous or semiautonomous network with smart contracts andcrytocurrency/tokenprocessing.ThinkNetflixwithoutthe'Netflix'orUberwithoutthe'Uber'.Dramaticcostsavingswilldrivethesesystemstomarketdominancewitheasyglobalscalability.Legacycompanieswithhierarchicalstructures [thinktraditionalbankingandmanyhealthcaremodels]willstruggletocompetewithadistributednetworksystem."Hierarchydoesn'tscale'asnotedbyblockchaintheoristMelanieSwan[52]. ThisapproachifmanagedcorrectlycouldworkwellwiththeservicesthatDeepRadiologyprovidesandallowaconsiderablecostsavingstothecustomers.

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7.DATAANDCURATION Working with deep learning in medical imaging requires massive amounts of highquality labeled data andDeepRadiology’s unique asset also includes a largemedical imagingdatarepositoryofoverhalfabillionlabeledmedicalimages.Thisdatastoreisincreasingwithterabytes of new data eachmonth. The optimally tagged data have enabled DeepRadiologyteamtorapidlymakesignificantadvancementinproductdevelopment. ThisdatautilizedbyDeepRadiologyforproductdevelopmentiscompletelyanonymized.The data does not have any confidential patient or source identification information.DeepRadiologyhasfullcontrolofthedata.Alldataisprivatelykeptoffline. ThedataissufficientforthecurrentDeepRadiologyproductdevelopment.Itincludesawide range of demographic spectrum, and technical scanner variability and protocols, thusallowing for the development of better products that can perform at high quality level indifferent facilities across the industry of medical imaging in the U.S. and around the world.DeepRadiology is working closely with other relationships to acquire additional data thatDeepRadiologydeemsusefulforfutureexpansionofproductlineandenhancementofproductquality. Curationorannotationoftheimagesinthedataisperformedbyexpertradiologistsandotherimagingspecialistswithataxonomyoflabelsspecificallydevelopedforourdeeplearningprocessesincludinghierarchicalloss.OurtaxonomiesadheretorequirementsfordeeplearningandAI,butalsohaveclearlydefinedmedical interpretations.Giventhesizeandscopeofthedataavailable,amethodto locatestudieswhichfitourannotationrequirementwasdevised.First, our taxonomies were mapped to specific keywords and phrases found in radiologistreports. Thesemappings allowedus to quickly find studies for our training set andprioritizeimageannotationbyexpertradiologists.

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8.INTELLECTUALPROPERTY Withthedevelopmentofsomuchnoveltechnologytoapplydeeplearningtomedicalimaging,DeepRadiologyhasfiledsevenpatentsforintellectualpropertyprotection.Inadditionseveralotherpatentsareinprocess. The various patents claims and covers technology for the development, training andimplementation of convolutional neural networks able to recognize abnormalities inmedicalimagescansandstudiesofthehumanbodiesandtogenerateandoutputinformationusefulinmedicaldiagnosis. Somealsocovermethodsmedicalimagingdatalabeling.Properandoptimallabelingofdataiscritical inordertoutilizedeeplearningtechnologyformedical imaging.Theprocess isverytimeconsumingandcostlyifdonebyhuman.Ourpatentsshowmethodsforautomatingthisprocess.

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9.OURPRODUCTS InNovember2017wereleasedagroundbreakingreportofthefirstartificialintelligencesystem to interpret computed tomography [CT] scans (CAT scans) with performance levelsgreaterthanthatofhumanradiologists[ref].Thesystemwasdevelopedandtrainedusingover9millionCT scan imagesof thehead.Toput that inperspective, theaverage radiologistwillonlyseeaverysmallfractionofthoseduringtrainingandinpractice.Thisproductwasfurtherrefinedusinginformationcontainedineverymajormedicaltextbookonthesubject. Analysis of medical imaging data is often one of the first steps in the diagnosis anddeterminationofcourseoftreatment,which insomecasesmustbedeterminedwithinafewminutes,makingthetimespentbytheradiologistonanalysisacriticalbottleneck.Assistedorautomatedanalysiscanhelpreducethetimenecessarytoarriveatadiagnosis. Inaddition,humanerrorduringroutinediagnosis isoftenunavoidableevenforhighlytrainedmedicalprofessionals, forexampleduetohumanfatigue, inattentionanddistraction.Nevertheless,sucherrorscanharmpatientsanddriveupthecostofmedicalcare,whichhasanadverseeffectonthehealthcaresystematlarge. Assistedorautomatedanalysiscanhelpreducetheerrorindiagnosis.Itcanalsomakehigh-qualitymedicalcarepossibleinsituationswherenohighlytrainedphysiciansareavailable,orwhere thecostof their serviceswouldbeprohibitive. In fact, theuseofmachine learningtechniquesallowsthebenefitsoftrainingandexperiencetobesharedgloballybyallsystems,ratherthaneachindividualsystembeingtrainedinisolation. In this discussion, we focus on computerized tomography (CT) as a representativeimagingmodality,andonthedetectionofclinicalpathologiessuchasintra-cranialbleedsasarepresentativetask.InparticularweconsiderCTstudiesofthehead(CThead),whereastudyisa collection of imaging data captured from the same subject during the same session, forinstance a collection of a few tens to hundreds of two-dimensional (2D) slices comprising avolumeimage.Ouraimistobuildasystemthatcangeneratereportsautomaticallyforalargefraction of CT head cases, while studies that our system does not generate reports for arereferred to a human radiologist. The goal of this system is not to replace the radiologistentirely,buttoreducehumanworkload.Thenetworkidentifiesstudieswhereitcangenerateareport with sufficient confidence, referring other cases, which may or may not be clinicallysignificant,toaradiologist.Inthispaper,wemeasuretheclinicallysignificantmissratewithinthestudiesthatarenotreferredtoaradiologist.Intuitively,oursystemcanbemadearbitrarilysafe simply by reducing the number of cases that are reported on to zero.While safe, thiswouldnotbeuseful.Aswehaveprogressed,thepercentageofstudiesonwhichthenetworkcanreportforabetter-than-humanerrorratehassteadilyincreased,thusmakingtheapproachviable to reduce the workload of human radiologists. For example, at literal error rates,

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DeepRadiologyNetreducestheloadonhumanradiologistsonover40%ofstudies.Inpractice,wechooseanoperatingpointconservatively,withsignificantlylowererrorthantheliteralerrorrateofUSBoardCertifiedradiologists. The types of discordance/disagreement of reports between radiologist is generallydividedintotwogroupsbasedonclinicalimpact:Clinicallysignificantandclinicallyinsignificantdiscordance[4].WearriveataliteraryerrorratebycollectingresultsfromfivesourceswhichmeetthecriteriaofreportingspecificerrorratesininterpretationofCTheadexaminationsbyboardcertifiedradiologists. [18]noteda4%clinicallysignificanterrorrate in137CTscansofthe head. [4] found that in the reporting of 716 CT scans of the head, therewas a clinicallysignificant error rate of 2% . [13] found a clinically significant error rate of 0.4% in theinterpretationof1081CTscansofthehead.Jordanetal.in2012reportedthatin560reportsofCThead,therewasa0.7%errorrate[14].[1]reportedasignificanterrorrateof2%for284CTscansofthehead.Fromtheresultsofthesefiveworks,wecalculatetheoverallerrorrateforthecombined2,778CTheadexaminationstobe1.21%throughweightedsummation. Fromthisoverallerrorrate,wewishtofindasuitableclinicallysignificantmissrate.[21]reported that 81%of errorswheremisses.When looking just at CThead interpretations, [5]notedamissrateof70%,however,thisstudyinvolvedresidentsintraining.Inalaterwork,[4]usedpracticingboardcertifiedradiologistswithheadCTinterpretationsfoundafalsenegativerateof68%.Weusethemostconservativeoftheserates,andgiventheoverallerrorrateof1.21% for CT head interpretation, of which 68% are clinically significant misses, arrive at aclinically significant miss rate (CSMR) of 0.83%, which we use for comparison to humanperformanceinthismanuscript. We have described a system and method to perform automated diagnosis ofpathologies fromCThead,developedover thecourseofmultipleyearsand firstdisclosedatRSNAinDecember2016.Sincethen,otherstudieshavebeenreportedinliteraturedescribingthe use of deep learning, and specifically deep convolutional networks, formedical imaginginterpretation. For themost part, these studies are too small to positively assess the clinicalviabilityofdeeplearningasanautomateddiagnostictool.Itsroleinbothautomatedaswellasassisted diagnostics remain to be fully validated. Even at comparable-to-human error rates,there may be advantages in deploying an automated system in a “second-opinion mode,”provideditsoperationdoesnotbiastheworkoftheradiologist,sincethefailuremodesofthetwoarecomplementary.Thereisampleevidencethatmosterrorsmadebytrainedandboard-certified radiologists is due to inattention, whereas automated diagnostics may miss rarepathologiesforwhichinsufficienttrainingdataisavailable,buttheydonotgetdistracted. Comparingtohumanradiologistsisnon-trivial.First,humansdefinegroundtruth,soitisnecessarytohavemultiplereadings,aswedo.Second,wearenotchoosingthe“best”subsetonwhichtoreport,butratherthenetworkitselfselectsautomaticallythestudiestoreporton.Choosing the“best”subset requiresgroundtruth tobedetermined,which thenetworkdoesnot have access to. Instead, it performs a selection based on its own confidence, which isdeterminedautomaticallywithoutaccesstothegroundtruth.Itisthenmeaningfultocompute

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errors in the confidence subset only because the non-confident cases are referred to aradiologist. Note that falsepositives are zeroby constructionas the systemonly reportsonhigh confidence negatives. Table1:ClinicallysignificantmissratesofDeepRadiologyNetandradiologist

DeepRadiologyNet is continuously being improved and updated, and we believe itsdeploymentwill result inbettercare: faster,moreaccurate thanhumans,and farmorecost-effective.Moreimportantly,itscontinuousimprovementanddeployablenaturemakeforhigh-qualitydiagnosisavailableinremoteorunder-servedregions,oreasingthebottleneckduetotheshortageofhighlytrainedspecialists. TheactualtimethataradiologisttakestointerpretasimpleCTscanoftheheadisontheorderof3-4minutes.Oursoftwarecanprocesssuchastudyinfractionof1second.Oursystemalsohasaclinicallysignificantmiss[error]rateconsiderablylowerthanthepublishedsimilarerrorratesforhumanradiologists.Finally,therepresentativepayment/costforprovidingsuchservicesintheUnitedStatesisapproximately$50.Thecostforoursystemtoprocesssuchastudyis$00.002. WearenowexpandingoursoftwaretointerpretothermajormedicalCTscantypesaswellasmagneticresonanceimaging[MRI],plainx-rays,ultrasound,mammographyandnuclearmedicine [Figure12].Ourproductsarenowbeginning tobedeployed tohospitalsandotherimagingfacilitiesintheUnitedStates.

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Figure12.ExamplesofotherDeepRadiologysystems.A.ChestX-rayB.MammogramC.UltrasoundD.CTHead

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10.CLINICALTRIAL In order to benchmark DeepRadiologyNet against CSMR by humans, we designed aretrospectiveclinicaltrial,conductedin2017,usingasetofstudiescompletelydisjointfromthetrainingandvalidationset. DeepRadiologyNet was trained on over 24,000 studies, containing approximately 3.5millionimagesandourtrialwasperformedon29,965studies,comprisingof4.8millionimages.Allmedicaldatawas strippedofany identifying information, storedand transmitted throughHIPAAcompliantprotocolsanddevices.Trial studiesoriginated fromover80sitesacross theglobeduringtwocontinuoustimeperiods:September2015throughDecember2015andMay2016throughSeptember2017.Imagingdatawascollectedfromover50typesofscannersfromallmajormanufacturersandincludespatients inallagegroupsfromnewbornsandinfantstogeriatricspatients.Incomingdatawaspre-processedbasedontheirDICOMdata,ensuringthattheyhavevalidheadersandpixeldata.Anydatawhich containedcorruptedDICOMheaderswereexcludedfromtheevaluation.Weusethemeta-dataintheDICOMheadertoselectaxialimages. These are submitted toDeepRadiologyNet and scoreswere generated. The label setproducedmirrors the hierarchical loss used for training, only a subset ofwhich is ultimatelyusedtorenderthefinaldecision,dependingonwhichclinicaltestisbeingconducted. Theanalysisisconductedatthelevelofastudy,ratherthanindividualimage,andthescoresofindividualimagesareaggregatedthroughthestudybasedontheuncertaintyestimateof the label distribution produced by the network, φwˆ(x), interpreted as a posterior scorerelativetothedistributionoflabelsinthehierarchy. Studies inourclinical trialwereexhaustivelyannotatedwith30nonmutuallyexclusivepathologicaltraitswhichweredividedbasedontheirclinicalsignificance.Examplesofclinicallyless significant traits include paranasal sinus disease, scalp swelling, old infarcts, and chronicage related findings [27, 4]. Significant traits include those that could affect immediatemanagement or have an adverse patient outcome such as acute intracranial hemorrhage,depressed skull fracture, acute infarction or intracranial mass. In our analysis, we look atperformance in predicting all types of traits, however we pay careful attention to clinicallysignificantfindings.Thedistributionofphenomenologicaltraits inourclinicaltrial isshowninFigure13.

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Figure13.ChordDiagramshowingtheco-occurrenceofphenomenologicaltraitswithinourtrialdatasetof29,965CTstudiesofthehead,consistingofover4.8millionimages.Table 2. Population density of clinical trial studies and those fully characterized byDeepRadiologyNet. The first column is the list of pathology ground-truth detected throughmultiplevalidationbyhumanspecialists(between2and5board-certifiedheadradiologists),aswellasclinicalfollow-throughandoutcomes.Thesecondcolumnisthetotal incidenceofthispathology inthetestset.Thefollowingtwocolumnsarethepercentageerrorsreportedbyanetworkwithoperatingpointchosentoreporton42.1%ofthecases,automaticallydeterminebythenetworkbasedonaconfidencescoregeneratedattesttime,andthesameforanetworkreportingon8.5%ofthecases.Thelastrowsindicateclinicallysignificanterrors,asdefinedanddescribedinthetext.

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For our clinical trial, we calculated clinically significant misses of all 29,965 studiesthroughoutcomeanalysisandconsensusof2andupto5radiologists forDeepRadiologyNet.DeepRadiologyNethadsufficientconfi-dencetocharacterizeandpredictall30traitsin8.5%ofstudiesinthetrialwithaCSMRof0.037%,aratethatisfarbelowtheliteralestimatedCSMRofboardcertifiedradiologistderivedintheprevioussection.Furthermore,atadifferentoperatingpoint,DeepRadiologyNethadconfidencetoreporton42.1%ofthestudieswithalowerCSMRthantheestimatedratefromliterature.Populationcharacterizationoftheclinicaltrialdataandstudies which our DeepRadiologyNet reported on are summarized in Table 2. ExamplelocalizationofthesepredictionsisdepictedinFigure14.Localizationwasperformedthroughaproprietarymethodbeyondthescopeofthismanuscript.

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Figure14:ExamplevisualizationusingourproprietarynetworkintrospectionmethodologywithCTscanimagesofthehead.Theabnormalitiesarecorrectlycolorizedandcircledbythesoftware. ThesearejusttwosampleoperatingpointsofDeepRadiologyNet.Weenvisionthatthechoiceofoperatingpointwillneedtotakeintoaccountavarietyoffactors includingdisease,modality, health-care provider, availability of human resources, geography and access tofacilities,amongotherconsiderations[Figures15-16].

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Figure15:ReceiveroperatingcurvemeasuringperformanceofDeepRadiogyNetondetectionofclinicallysignificantpathologiesonthetrialdatasetof29,965studies,comprisingover4.8millionimages.95%Confidenceintervalsaredisplayedasribbonoverlays,whichwerecomputedthroughbootstrapre-samplingofthedata.

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Figure16:ReceiveroperatingcurvemeasuringperformanceofDeepRadiogyNetondetectionof30traitsonthetrialdatasetof29,965studies,comprisingover4.8millionimages.95%Confidenceintervalsaredisplayedasribbonoverlays,whichwerecomputedthroughbootstrapre-samplingofthedata.

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11.MARKET Accordingto2012datafromtheWorldHealthOrganization,globaltotalhealthcarecosthasrisentoUSD6.5trillion,puttingtheglobalcostofmedicalimagingataboutUSD650billionwiththeU.S.remainingthebiggestspenderaccountingforhalfoftheglobalmedical imagingexpenditure.

Within medical imaging, report interpretation is widely known to account forapproximately25%oftheoverallcostthusputtingthemarketformedicalinterpretationscanalonetobeoverUSD80billionintheU.S.andoverUSD160billionworldwide.

These figures are projected to continue increasing in a 2017 global healthcareexpenditureanalysisbyDeloitte[Figure1][34].

Figure1.Globalhealthcareexpenditure-Ref:Deloitte[34]

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12.SECURITY The DeepRadiology system software operates on machines accessed via a securerepresentationalstatetransfer(REST)webapplicationprogramminginterface(API).Alldataincontact with the system is anonymized data and thus contains no identifiable patientinformation. HIPAA (Health Insurance Portability and Accountability Act of 1996), is a United Stateslegislationthatprovidesdataprivacyandsecurityprovisionsforsafeguardingmedicalinformation,particularly in hospitals and clinics’ ElectronicMedical Records (EMR). Similar regulations are ineffect in other international jurisdictions. Digital Imaging and Communications in Medicine(DICOM) is a standard for storing and transmitting medical images enabling the integration ofmedical imagingdevicessuchasscanners,servers,workstations,printers,networkhardware,andpicturearchivingandcommunicationsystems(PACS)frommultiplemanufacturers. All the data transferred to DeepRadiology systems is accomplished using standardacceptedpracticesforsecureandfullyHIPAAcomplianttransferofDICOMmedicaldata.Thisisdoneutilizingmilitarygradeencryptionusingvirtualprivatenetworks (VPN)or securesocketlayer (SSL) protocol. The imaging data are then processed by the system and an outputmessageisproducedandreturnedviathesecureAPI. The official address for DeepRadiology is 2461 Santa Monica Blvd, Suite 105, SantaMonica, California 90404 which is a mailing address. For security reasons, due to theconfidentialnatureofthemassiveamountsofmedicaldatathatwehouse,wedonotacceptvisitorstoouractualofficesexceptbyappointment.

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13.ROADMAPTOKENUSAGEROADMAP2018March15 TokenPreSaleBegins2018April15 TokenPreSaleends2018May1 TokenMainSaleBegins2018May15 TokenMainSaleEnds2018July1 Accepttokensfordiscountedinternationalpayments2018August1 Accepttokensfordiscountedpaymentsforservices2019January PrototypeofdecentralizedFOGGPUprocessingsystem2019June Prototypeofdistributedsemiautonomoussystem

OVERALLROADMAP[INCLUDINGUSEOFVENTUREFUNDS] 2018January BeginDeploymentofProductstoUSHealthcareInstitutions2018January ContinueDevelopmentofNewProducts2018March15 TokenPreSaleBegins2018April15 TokenPreSaleends2018May1 TokenMainSaleBegins2018May15 TokenMainSaleEnds2018July1 BeginDeploymentofServicestoInternationalInstitutions Accepttokensfordiscountedinternationalpayments2018August1 Completesmartcontractsystemforbilling,paymentprocessing Accepttokensfordiscountedpaymentsforservices2019January PrototypeofdecentralizedFOGGPUprocessingsystem2019June Prototypeofdistributedsemiautonomoussystem

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14.REGULATORY TheFoodandDrugAdministration(FDA)isafederalagencyoftheUnitedStatesDepartmentofHealthandHumanServices,oneoftheUnitedStatesfederalexecutivedepartments.TheFDAisresponsibleforprotectingandpromotingpublichealththroughthecontrolandsupervisionoffoodsafety,tobaccoproducts,dietarysupplements,prescriptionssupplementsandover-thecounterpharmaceuticaldrugs(medications),vaccines, biopharmaceuticals,bloodtransfusions,medicaldevices,electromagneticradiationemittingdevices(ERED),cosmetics,animalfoods&feedandveterinaryproducts. DeepRadiology is in theprocessofobtainingU.S.FoodandDrugAdministration (FDA)product clearance. Similar approvals by various countries in Europe and Asia are also inprogress. DeepRadiology is working through the process diligently with the authorities andexpectstoreceiveclearancewithinthecomingmonths.AsmoreDeepRadiologyproductsaredevelopedor improvedwewill continue to file additional submissions to the FDAandotheragenciesformoreclearanceofourcomprehensiveproductlineaddressingthewholespectrumofmedicalimaging.

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15.TOKENDESIGN Ablockchaintoken isadigitaltokencreatedonablockchainaspartofadecentralizedsoftware protocol. There are many different types of blockchain tokens, each with varyingcharacteristics and uses. Some blockchain tokens, like Bitcoin, function as a digital currency.Otherscanrepresenta right to tangibleassets likegoldor realestate.Blockchain tokenscanalsobeusedinnewprotocolsandnetworkstocreatenewservicesanddistributedapplications.ThesetokensaresometimesalsoreferredtoasUtilityTokens. Thereareanumberof challenging legalquestions surroundingblockchain tokens. Forexample, some tokens, depending on their features, may be subject to US federal or statesecuritieslaws[41].Thiswouldmean,amongotherthings,thatitisillegaltoofferthemforsaleto US residents except by registration or exemption. Similar rules apply in many othercountries.Inordertoclarifythissituation,theUSSupremeCourtcreatedthe"HoweyTest"fordeterminingwhethercertain transactionsqualifyas "investmentcontracts." If so, thenunderthe Securities Act of 1933 and the Securities Exchange Act of 1934, those transactions areconsideredsecuritiesandthereforesubjecttocertaindisclosureandregistrationrequirements. With this inmind, new blockchain tokens and their applications, if designed properlyaccordingtotheHoweyTestparameters,shouldbedeemedasasimplecontract,equivalenttoafranchise agreement. By this measure, holders of a blockchain token are granted rights tocontribute to a larger system, “rather than through a passive investment interest.” Theadvantageofclassifyingsuchtokensasasimplecontractdisentanglestokenownershipfromthelegal complexities of holding a security. It further encouragesthe inevitable shift towardsdecentralization,improvingtheefficiencyandsecurityofdigitalprotocols. WehavedesignedourDRADTokensandoverall systemwith theHoweytest inmind.OurtokenisaproperutilityTokencreatedforDeepRadiology. Accordingtoourcalculations,theHoweyTestscorefortheDeepRadiologyTokenis20whichmeansthatitis'unlikely'tobeconsideredasecurity[42]. The DeepRadiology DRAD token is created using smart contracts using the Ethereumblockchain.ItisimplementedaccordingtotheERC20EthereumTokenStandard,alsoknownasStandardizedContractAPI.TheERC20standarddefinessixdifferentfunctionsforthebenefitofother tokens within the Ethereum system. These are generally basic functionality issues,includinghowtokensaretransferredandhowuserscanaccessdataaboutatoken.ERC20alsoprescribestwodifferentsignalsthateachtokentakesonandwhichothertokensareattunedto. Puttogether,thissetoffunctionsandsignalsensuresthatEthereumtokensofdifferenttypeswill typicallywork the same inanyplacewithin theEthereumsystem.Thismeans thatalmost all of the wallets which support the Ether currency also support ERC20 compliant

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tokens.ERC20compatibilityensuresthatintegrationwithexchangesandotherserviceswillbeno harder than for any other Ethereum-based token. The smart contract methodology alsoincreasesthesafetyofinvestment. ThesupplyoftheDeepRadiologyDRADtokenisthetotalnumbersoldinthesaleandanequivalentamountsetaside for thereserve. Although100milliontokenswillbecreated,allunused tokenswill be burned. The token is defined in a cryptographically binding EthereumERC20setofsmartcontractsandisthesolemediumforsettlement(purchase,incentives,etc.)within the ecosystem. The token will be divisible to 18 decimal places to allow maximumflexibilityforfuturetransactions. WhiletheDeepRadiologyDRADtoken isbasedonEthereumtechnology, investmentcanalsobemadewithUSD,EUR,BTC,andotherfiatcurrenciesthroughawiretransfer.ThepriceofthetokeninETHwillbefixeddailywithbasicpricerelativetoEtherduringthePresalesandMainSale.Itwillbesettoapproximately$0.50/tokenduringthepresaleand$1.00duringtheMainSale. Afterthatthetokenprice indollarswilldependontheEther/dollarexchangerate ThetokenmaybeusedtopurchaseservicesfromDeepRadiology.Thechargesfortheseserviceswillbediscountedfromtheratesfortheservicespaidinothercurrenciesduetothefollowingmechanisms: Payment in foreigncurrencies incursadditional chargesdue tobankingexchange feesand other tariffs. By accepting payments in the form of our utility tokens we remove theexpenseof these intermediaries.Wewill offer thisoption toour foreign customerswhowillhave discounted pricing for payments using the utility tokens. This will have the effect ofpassingonthesavingsduetotheseefficienciestoourcustomersaswellasmaintainingvaluefortheutilitytokens. Secondly,thereisconsiderableoverheadinaccounting,billing,paymentprocessing,andcollectionfeesinourservicesaswithmostradiologyandotherhealthcareserviceproviders.Byacceptingpaymentsintheformofourutilitytokenswhichwillthenbeprocessedusingsmartcontractstoautomaticallyreleasetheservicesandperformaccounting,wecandisintermediatethese other providers and further lower the cost of our services. This will be provided as alowercostoption forourcustomerswhochoose touse theutility tokens.As in theexampleabove thiswillalsohave theeffectofpassingon thesavingsdue to theseefficiencies toourcustomersaswellastendingtomaintainvaluefortheutilitytokens. The tokenswill alsobeused in theDeepRadiology community/ecosystemofpatients,doctors,developers,andotherswhoweactivelyrewardfortheircontributions.Thiscanbeinthe formof a 'bugbounty' fordeveloperswho identify issueswith software thatneed tobecorrected or other members who give us reviews and feedback on other aspects of thecommunity.Wealsoanticipaterewardingupvotedcontributionsaswellasseekingcommunityguidanceonprioritizingnewdirectionsfordevelopmentandproductsandservices.Byutilizing

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ourutilitytokenfortheserewardswecaneliminatethecostsofhavingtopayintermediariesforbankingservices,wires,andexchangesinthecaseofforeigncurrency.Inadditiontheutilitytokensgiveusmuchgreaterflexibilityinpaymentamountsbyallowingmicropayments. Next, by creating a blockchain based FOG decentralized processing network thepayments thatpreviouslywent tothe legacyprocessingproviderscouldnowbepaidoutourown communitymembers [in the form of tokens] whowould choose to provide processingpower.Thelaststepinourplanbeyondefficienciesoftokenizedbusinesstransactions,migrationtoFOGcomputing[tosharerevenuewithourcommunityandimprovereliability/security]istomovetofullyautonomousorsemiautonomousorganizationsforourproductsthataresufficientlymaturetoruninthatmode.ForexamplewhenaCTheadinterpretationsystemissufficientlydevelopedandrobust,onecouldimagineascenariowhereitwouldbe'released'toruninsuchamodeandrealizetremendouscostsavingstothecustomeraswellasotheradvantages[Figure11].

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16.TOKENSALELOGISTICS**Pleaseregisteratwww.deepradiology.comtoparticipateinTokenSale.****TobewhitelistedfortheTokenPreSalepleaseemailusatwhitelist@deepradiology.comwithyourrequestedinvestmentamount**TheuseofKYC(knowyourcustomer)isalwaysabalancebetweenthefreedomofanonymoususeofcryptocurrenciesandtheabilitytointeractwithotherbusinesses,bankinginstitutionsandallcountries.WehavemadethedecisiontouseKYConallpurchasesoftokensforthegreatergoodandlongtermsuccessoftheDeepRadiologycommunity.Thisisbasedonseveralfactors:1.CertaincryptocurrencyexchangeshavesuggestedtheymayexcludecryptocurrenciesthatdidnotproperlyimplementKYCprocesses.Thus,notimplementingKYCcouldposealong-termrisktoourproject.2.WithproperKYCprocessesinplacethenitwillbepossibleforallpartiestoestablishcredibilitywithbanksandfollowAnti-MoneyLaunderingregulations.VoluntarycompliancethusgivesDeepRadiologyanditsparticipantsastampoflegitimacywithregulatorsandbanks.3.KYCcompliancemayhelpDeepRadiologyreachalargerglobalaudienceandexpandthenumberofjurisdictionsinwhichwecanoperate.SuchcomplianceallowsourcommunitytoincludeinvestorsintheUnitedStates,Britain,Canadaandelsewhere.4.TheUSSecuritiesandExchangeCommission(SEC)isreportedlypreparingtoprosecuteICOswhichareheldwithoutKYCprocedures.Infact,therehavebeencaseswheretheSECprosecutesanddemandsrefundsforTokenSalesthathavenotimplementedKYC.InSeptember,decentralizedapplicationProtostarrmayhavebeenthefirsttokentoceaseoperationsduetocommunicationfromtheSEC,signalinganintensificationofregulatoryscrutiny.VoluntarilycompliancewithKYCregulationsprovidesmanyadvantagestoDeepRadiologyanditsinvestors,evenifwearenotcurrentlyexplicitlymandatedtoenactsuchaprocessinalljurisdictions.Ultimately,itisintheinvestors’bestinterestthatKYCiscarriedoutproperly.Formaximumsecurity,allKYCclearancewillbeperformedbyDeepRadiologyratherthanthroughsomethirdparty.AspartofourKYCprocesswearerequestingthefollowing:Forindividual:Twodocumentsfromanyonewishingtotakepartinthetokensale:(i)OneisagovernmentissuedphotoIDAND

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(ii)Thesecondisaproofofresidencedocument(Examples:UtilitybillORBankstatementORTaxstatement].OncethesetwodocumentsareuploadedtoDeepRadiologywebsiteandapproved,tokenpurchasesmaytakeplace.Forcorporation:Threedocumentsfromanycorporatewishingtotakepartinthetokensale:(i)OneisagovernmentissuedphotoIDoftheCorporateAuthorizedAgent(Examples:Owner,Director,CEO,President,AuthorizedSecretary).AND(ii)Thesecondisaproofofalegitimateaddress(Examples:CorporateorAuthorizedagentpersonalUtilitybillORBankaccountORTaxstatement)AND(iii)ThethirdistheCorporateRegistrationdocumentORBusinessLicense.OncethesethreedocumentsareuploadedtoDeepRadiologywebsiteandapproved,tokenpurchasesmaytakeplace.TokenPreSaleBegindate: March15,201808:00:00PacificStandardTimeEnddate: April15,201820:00:00PacificStandardTimePrice: PreSaleDRADTokenswillbepricedtoapproximatelyUSD0.50

inEtherpriortoPreSaleTokenstandard: ERC20MinimumSale: Investmentfundswillbereturnedif100,000tokensarenotsoldMaximumCap: WillbedeterminedpriortosaleandlimitedbysmartcontractPurchasemethods: ETH,BTCorbankwireareacceptedpurchasingmethods.

Inquireforotheroptions **TobewhitelistedfortheTokenPreSale-pleaseemailusat

whitelist@deepradiology.comwithyourrequestedinvestmentamount.**

TokenMainSaleBegindate: May1,201808:00:00PacificStandardTimeEnddate: May15,201820:00:00PacificStandardTimePrice: MainSaleDRADTokenswillbepricedtoapproximatelyUSD

1.00inEtherpriortoMainSaleTokenstandard: ERC20MinimumSale: Investmentfundswillbereturnedif1,000,000tokensarenotsoldMaximumCap: Willbedeterminedpriortosaleandlimitedbysmartcontract.

***Allunsoldtokenswillbeburned.***

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Purchasemethods: ETH,BTCorbankwireareacceptedpurchasingmethods.Inquireforotheroptions.

MinimumETHpurchase: NoneMinimumwirepurchase: USD$1000MaximumPurchase: None TokenTradingOurDRADtokensare implementedaccording to theERC20EthereumTokenStandard,alsoknownasStandardizedContractAPI.ThisensuresthatEthereumtokensofdifferenttypeswilltypicallyworkthesameinanyplacewithintheEthereumsystem.Thismeansthatalmostallofthe wallets which support the Ether currency also support ERC20 compliant tokens. ERC20compatibilityensuresthatintegrationwithexchangesandotherserviceswillbenoharderthanforanyotherEthereumbasedtoken.WeexpecttolistourDRADtokensonexchangesassoonaspossibleafterthecloseofthecrowdsale.Thisinformationwillbepostedonourwebsiteassoonasitisavailable.TokenAllocation100,000,000DRADtokenswillbeinitiallycreatedforthesale.

• 100,000,000DRADtokenswillbeavailableintheCrowdsale.***Allunsoldtokensinthispoolwillbeburned.***

• AnamountofDRADtokensequaltotheamountsoldintheCrowdsalewillbekeptinreserveandbeusedforTeammembersandCommunityDevelopment.Alltokensinthispoolexceptfor5milliontokenswillbelockeddownwith4yearvestingandaoneyearcliff.The5milliontokenswillbeusedforimmediatecommunitydevelopment.

UseofFunds100% CommunityDevelopmentandBlockchain/SmartContractDeployment.

Allsaleproceedswillbededicatedtothefurthergrowthoftheproject.

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17.DISCLAIMERS,DISCLOSURES,ANDRISKThiswhitepaperismeanttodescribethecurrentlyanticipatedplansofDeepRadiology,Incanditsaffiliates,(togetheras“DeepRadiology"),fordevelopinganewblockchaintokenmechanism,(“DeepRadiology Token"), that will be used on the network sponsored by DeepRadiology,(“DeepRadiologyNetwork").DeepRadiologymay fromtimeto timerevise thisWhitePaper inany respectwithoutnotice.Nothing in this document should be treated or read as a guarantee or promise of howDeepRadiology'sbusiness,theNetwork,ortheTokenswilldeveloporoftheutilityorvalueoftheNetworkortheTokens.ThisWhite Paper outlinesDeepRadiology's current plans,which could change at any time atDeepRadiology’s discretion, and the success of which will depend on many factors outsideDeepRadiology's control, including market- based factors and factors within the data andcryptocurrencyindustries,amongothers.AnystatementsaboutfutureeventsarebasedsolelyonDeepRadiology'sanalysisoftheissuesdescribedinthisdocument.Thatanalysismayprovetobeincorrect.ThisdocumentdoesnotconstituteanofferorsaleoftheTokensoranyothermechanismforpurchasing the Tokens (such as, without limitation, a fund holding the Tokens or a simpleagreement for future tokens related to the Tokens). Any offer or sale of the Tokens or anyrelatedinstrumentwilloccuronlybasedondefinitiveofferingdocumentsfortheTokensortheapplicable instrument. Purchasing the Tokens or any related instrument is subject to manypotentialrisks.Someoftheseriskswillbedescribedintheofferingdocuments.These documents, alongwith additional information about DeepRadiology and theNetwork,are available on our website at www.DeepRadiology.com. Purchasers of Tokens and relatedinstrumentscouldloseallorsomeofthevalueofthefundsusedfortheirpurchases.This document is not a securities offering or polled investment plan. It does not requireregistrationorapprovalbytheSECoranyotherorganization.Participantsarerecommendedtoscrutinizethisdocumentandmakeprudentinvestments.

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