Pediatric Radiology

, Volume 49, Issue 11, pp 1384–1390 | Cite as

Artificial intelligence applications for pediatric oncology imaging

  • Heike Daldrup-LinkEmail author
Pediatric oncologic imaging


Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community.


Artificial intelligence Cancer Children Imaging Machine learning Oncology 



This work was supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant number R01 HD081123-01A1.

Compliance with ethical standards

Conflicts of interest



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Radiology, Lucile Packard Children’s Hospital, Pediatric Molecular Imaging ProgramStanford University School of MedicineStanfordUSA
  2. 2.Department of Pediatrics, Hematology/Oncology SectionStanford University School of MedicineStanfordUSA

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