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

Trevor J. Huff, Parker E. Ludwig, Jorge M. Zuniga

Research output: Contribution to journalReview article

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)349-356
Number of pages8
JournalExpert Review of Medical Devices
Volume15
Issue number5
DOIs
StatePublished - May 4 2018

Fingerprint

Learning algorithms
Learning systems
Printing
Anatomic Models
Costs and Cost Analysis
Planning
Image segmentation
Costs
Technology
Delivery of Health Care
Image acquisition
Cost reduction
Research
Patient Selection
Biomedical Research
Three Dimensional Printing
Machine Learning

Keywords

  • 3D manufacturing
  • 3D printing
  • additive manufacturing
  • anatomical modeling
  • artificial intelligence
  • automated image segmentation
  • computer-aided manufacturing
  • convolutional neural network
  • machine learning
  • medical image segmentation
  • personalized medicine
  • surgical model
  • surgical planning
  • three-dimensional printing

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering

Cite this

The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning. / Huff, Trevor J.; Ludwig, Parker E.; Zuniga, Jorge M.

In: Expert Review of Medical Devices, Vol. 15, No. 5, 04.05.2018, p. 349-356.

Research output: Contribution to journalReview article

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