@article {Currie44, author = {Geoffrey M. Currie}, title = {Intelligent Imaging: Developing a Machine Learning Project}, volume = {49}, number = {1}, pages = {44--48}, year = {2021}, doi = {10.2967/jnmt.120.256628}, publisher = {Society of Nuclear Medicine}, 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){\textendash}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.}, issn = {0091-4916}, URL = {https://tech.snmjournals.org/content/49/1/44}, eprint = {https://tech.snmjournals.org/content/49/1/44.full.pdf}, journal = {Journal of Nuclear Medicine Technology} }