| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
A comparison of bone age assessments using automated and manual methods in children of Indian ethnicity
|
Oza, Chirantap
|
|
|
52 |
11 |
p. 2188-2196
|
article
|
| 2 |
Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know
|
Meshaka, Riwa
|
|
|
52 |
11 |
p. 2101-2110
|
article
|
| 3 |
A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data
|
Ali, Redha
|
|
|
52 |
11 |
p. 2227-2240
|
article
|
| 4 |
Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists
|
Nguyen, Toan
|
|
|
52 |
11 |
p. 2215-2226
|
article
|
| 5 |
Current and emerging artificial intelligence applications for pediatric abdominal imaging
|
Dillman, Jonathan R.
|
|
|
52 |
11 |
p. 2139-2148
|
article
|
| 6 |
Current and emerging artificial intelligence applications for pediatric interventional radiology
|
Desai, Sudhen B.
|
|
|
52 |
11 |
p. 2173-2177
|
article
|
| 7 |
Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology
|
Offiah, Amaka C.
|
|
|
52 |
11 |
p. 2149-2158
|
article
|
| 8 |
Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective
|
Schalekamp, Steven
|
|
|
52 |
11 |
p. 2120-2130
|
article
|
| 9 |
Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence
|
Monah, Suranna R.
|
|
|
52 |
11 |
p. 2111-2119
|
article
|
| 10 |
Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating
|
Tsai, Andy
|
|
|
52 |
11 |
p. 2206-2214
|
article
|
| 11 |
Density map estimation with convolutional neural networks to count radiopaque markers on colonic transit studies
|
Tsai, Andy
|
|
|
52 |
11 |
p. 2178-2187
|
article
|
| 12 |
Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement
|
Kim, Min Jong
|
|
|
52 |
11 |
p. 2197-2205
|
article
|
| 13 |
How does artificial intelligence in radiology improve efficiency and health outcomes?
|
van Leeuwen, Kicky G.
|
|
|
52 |
11 |
p. 2087-2093
|
article
|
| 14 |
Introduction to the artificial intelligence in pediatric radiology imaging special issue
|
Offiah, Amaka C.
|
|
|
52 |
11 |
p. 2063-2064
|
article
|
| 15 |
The augmented radiologist: artificial intelligence in the practice of radiology
|
Sorantin, Erich
|
|
|
52 |
11 |
p. 2074-2086
|
article
|
| 16 |
The current and future roles of artificial intelligence in pediatric radiology
|
Otjen, Jeffrey P.
|
|
|
52 |
11 |
p. 2065-2073
|
article
|
| 17 |
The requirements for performing artificial-intelligence-related research and model development
|
Pareek, Anuj
|
|
|
52 |
11 |
p. 2094-2100
|
article
|
| 18 |
The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging
|
Taylor, Andrew M.
|
|
|
52 |
11 |
p. 2131-2138
|
article
|
| 19 |
The role of artificial intelligence in paediatric neuroradiology
|
Pringle, Catherine
|
|
|
52 |
11 |
p. 2159-2172
|
article
|
Journal description