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

Michael M. Moore; Ramesh S. Iyer; Nabeel I. Sarwani; Raymond W. Sze

doi:10.1007/s00247-021-05072-1

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

Michael M. Moore, Ramesh S. Iyer, Nabeel I. Sarwani, Raymond W. Sze

Research output: Contribution to journal › Review article › peer-review

6 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	367-373
Number of pages	7
Journal	Pediatric Radiology
Volume	52
Issue number	2
DOIs	https://doi.org/10.1007/s00247-021-05072-1
State	Published - Feb 2022

All Science Journal Classification (ASJC) codes

Pediatrics, Perinatology, and Child Health
Radiology Nuclear Medicine and imaging

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

abstract = "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.",

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

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