• Michael M Moore, Einat Slonimsky, Aaron D Long, Raymond W Sze, and Ramesh S Iyer.
• Department of Radiology, Penn State Health, Mail Code H066, 500 University Drive, P.O. Box 850, Hershey, PA, 17033-0850, USA. mmoore5@pennstatehealth.psu.edu.
• Pediatr Radiol. 2019 Apr 1; 49 (4): 509-516.

AbstractMachine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting. We also provide an expanded discussion of the challenges of implementing machine learning in children's imaging. These include the requirement for very large data sets, the need to accurately label these images with a relatively small number of pediatric imagers, technical and regulatory hurdles, as well as the opaque character of convolution neural networks. We review machine learning cases in radiology including detection, classification and segmentation. Last, three pediatric radiologists from the Society for Pediatric Radiology Quality and Safety Committee share perspectives for potential areas of development.

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