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Pediatric Radiology
Published in: Pediatric Radiology 11/2019

01-10-2019 | Artificial Intelligence | Pediatric oncologic imaging

Artificial intelligence applications for pediatric oncology imaging

Author: Heike Daldrup-Link

Published in: Pediatric Radiology | Issue 11/2019

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Abstract

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.
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Metadata
Title
Artificial intelligence applications for pediatric oncology imaging
Author
Heike Daldrup-Link
Publication date
01-10-2019
Publisher
Springer Berlin Heidelberg
Published in
Pediatric Radiology / Issue 11/2019
Print ISSN: 0301-0449
Electronic ISSN: 1432-1998
DOI
https://doi.org/10.1007/s00247-019-04360-1

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