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dc.contributor.authorHuff T.J.
dc.contributor.authorLudwig P.E.
dc.contributor.authorZuniga J.M.
dc.date.accessioned2020-09-02T22:20:33Z
dc.date.available2020-09-02T22:20:33Z
dc.date.issued2018
dc.identifier10.1080/17434440.2018.1473033
dc.identifier.citation15, 5, 349-356
dc.identifier.issn17434440
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4916
dc.descriptionIntroduction: 3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed. Areas covered: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints. Expert commentary: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
dc.language.isoen
dc.publisherTaylor and Francis Ltd
dc.subject3D manufacturing
dc.subject3D printing
dc.subjectadditive manufacturing
dc.subjectanatomical modeling
dc.subjectartificial intelligence
dc.subjectautomated image segmentation
dc.subjectcomputer-aided manufacturing
dc.subjectconvolutional neural network
dc.subjectmachine learning
dc.subjectmedical image segmentation
dc.subjectpersonalized medicine
dc.subjectsurgical model
dc.subjectsurgical planning
dc.subjectthree-dimensional printing
dc.subject3D printers
dc.subjectArtificial intelligence
dc.subjectClinical research
dc.subjectComputer aided instruction
dc.subjectComputer aided manufacturing
dc.subjectCost reduction
dc.subjectEngineering education
dc.subjectImage enhancement
dc.subjectImage segmentation
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMedical imaging
dc.subjectNeural networks
dc.subjectSurgery
dc.subject3-D printing
dc.subjectAnatomical modeling
dc.subjectConvolutional neural network
dc.subjectPersonalized medicines
dc.subjectSurgical planning
dc.subjectMedical image processing
dc.subjectclinical practice
dc.subjectcost
dc.subjecthuman
dc.subjectimage segmentation
dc.subjectlearning algorithm
dc.subjectoutcome assessment
dc.subjectReview
dc.subjectthree dimensional printing
dc.subjecttreatment planning
dc.subjectalgorithm
dc.subjectanatomic model
dc.subjectmachine learning
dc.subjectsurgery
dc.subjecttime factor
dc.subjectAlgorithms
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectModels, Anatomic
dc.subjectPrinting, Three-Dimensional
dc.subjectSurgical Procedures, Operative
dc.subjectTime Factors
dc.titleThe potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning
dc.typeReview


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