Intelligent Imaging: Developing a Machine Learning Project

J Nucl Med Technol. 2021 Mar;49(1):44-48. doi: 10.2967/jnmt.120.256628. Epub 2020 Dec 24.

Abstract

Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based initiatives using artificial neural networks before progressing to more complex deep learning (DL) approaches using convolutional neural networks. Several strategies and examples of entry-level projects are outlined, including mock potential projects using convolutional neural networks toward which we can progress. The examples provide a narrow snapshot of potential applications designed to inspire readers to think outside the box at problem solving using AI and ML. The simple and resource-light ML approaches are ideal for problem solving, are accessible starting points for developing an institutional AI program, and provide solutions that can have a significant and immediate impact on practice. A logical approach would be to use ML to examine the problem and identify among the broader ML projects which problems are most likely to benefit from a DL approach.

Keywords: artificial intelligence; artificial neural network; machine learning; nuclear medicine.

MeSH terms

  • Artificial Intelligence*
  • Machine Learning*
  • Neural Networks, Computer