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The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning
dc.contributor.author | Huff T.J. | |
dc.contributor.author | Ludwig P.E. | |
dc.contributor.author | Zuniga J.M. | |
dc.date.accessioned | 2020-09-02T22:20:33Z | |
dc.date.available | 2020-09-02T22:20:33Z | |
dc.date.issued | 2018 | |
dc.identifier | 10.1080/17434440.2018.1473033 | |
dc.identifier.citation | 15, 5, 349-356 | |
dc.identifier.issn | 17434440 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/4916 | |
dc.description | Introduction: 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.iso | en | |
dc.publisher | Taylor and Francis Ltd | |
dc.subject | 3D manufacturing | |
dc.subject | 3D printing | |
dc.subject | additive manufacturing | |
dc.subject | anatomical modeling | |
dc.subject | artificial intelligence | |
dc.subject | automated image segmentation | |
dc.subject | computer-aided manufacturing | |
dc.subject | convolutional neural network | |
dc.subject | machine learning | |
dc.subject | medical image segmentation | |
dc.subject | personalized medicine | |
dc.subject | surgical model | |
dc.subject | surgical planning | |
dc.subject | three-dimensional printing | |
dc.subject | 3D printers | |
dc.subject | Artificial intelligence | |
dc.subject | Clinical research | |
dc.subject | Computer aided instruction | |
dc.subject | Computer aided manufacturing | |
dc.subject | Cost reduction | |
dc.subject | Engineering education | |
dc.subject | Image enhancement | |
dc.subject | Image segmentation | |
dc.subject | Learning algorithms | |
dc.subject | Learning systems | |
dc.subject | Medical imaging | |
dc.subject | Neural networks | |
dc.subject | Surgery | |
dc.subject | 3-D printing | |
dc.subject | Anatomical modeling | |
dc.subject | Convolutional neural network | |
dc.subject | Personalized medicines | |
dc.subject | Surgical planning | |
dc.subject | Medical image processing | |
dc.subject | clinical practice | |
dc.subject | cost | |
dc.subject | human | |
dc.subject | image segmentation | |
dc.subject | learning algorithm | |
dc.subject | outcome assessment | |
dc.subject | Review | |
dc.subject | three dimensional printing | |
dc.subject | treatment planning | |
dc.subject | algorithm | |
dc.subject | anatomic model | |
dc.subject | machine learning | |
dc.subject | surgery | |
dc.subject | time factor | |
dc.subject | Algorithms | |
dc.subject | Humans | |
dc.subject | Machine Learning | |
dc.subject | Models, Anatomic | |
dc.subject | Printing, Three-Dimensional | |
dc.subject | Surgical Procedures, Operative | |
dc.subject | Time Factors | |
dc.title | The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning | |
dc.type | Review |