Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have be...

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Bibliographic Details
Published in:Pediatric radiology 2022-02, Vol.52 (2), p.367-373
Main Authors: Moore, Michael M., Iyer, Ramesh S., Sarwani, Nabeel I., Sze, Raymond W.
Format: Article
Language:English
Subjects:
Adult
Adults
Artificial Intelligence
Child
Humans
Image processing
Image segmentation
Imaging
Magnetic Resonance Imaging
Magnetic Resonance Spectroscopy
Medical imaging
Medicine
Medicine & Public Health
Neuroradiology
Nuclear Medicine
Oncology
Optimization
Pediatric Body MRI
Pediatrics
Radiography
Radiology
Ultrasound
Workflow
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Summary:Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.
ISSN:0301-0449
1432-1998
DOI:10.1007/s00247-021-05072-1