The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning

dc.creatorHuff T.J.
dc.creatorLudwig P.E.
dc.creatorZuniga J.M.
dc.date.accessioned2020-09-02T22:20:33Z
dc.date.accessioned2022-11-08T20:22:32Z
dc.date.available2020-09-02T22:20:33Z
dc.date.available2022-11-08T20:22:32Z
dc.date.created2020-09-02T22:20:33Z
dc.date.issued2018
dc.identifier15, 5, 349-356
dc.identifier17434440
dc.identifierhttps://hdl.handle.net/20.500.12728/4916
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5144463
dc.languageen
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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