Skip to main content
Top
Published in: Pediatric Radiology 11/2022

27-05-2021 | Artificial Intelligence | Artificial intelligence in pediatric radiology

The current and future roles of artificial intelligence in pediatric radiology

Authors: Jeffrey P. Otjen, Michael M. Moore, Erin K. Romberg, Francisco A. Perez, Ramesh S. Iyer

Published in: Pediatric Radiology | Issue 11/2022

Login to get access

Abstract

Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.
Literature
1.
go back to reference West E, Mutasa S, Zhu Z, Ha R (2019) Global trend in artificial intelligence–based publications in radiology from 2000 to 2018. AJR Am J Roentgenol 213:1204–1206PubMedCrossRef West E, Mutasa S, Zhu Z, Ha R (2019) Global trend in artificial intelligence–based publications in radiology from 2000 to 2018. AJR Am J Roentgenol 213:1204–1206PubMedCrossRef
2.
go back to reference Moore MM, Slonimsky E, Long AD et al (2019) Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatr Radiol 49:509–516PubMedCrossRef Moore MM, Slonimsky E, Long AD et al (2019) Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatr Radiol 49:509–516PubMedCrossRef
4.
go back to reference Cherukuri V, Ssenyonga P, Warf BC et al (2018) Learning based segmentation of CT brain images: application to postoperative hydrocephalic scans. IEEE Trans Biomed Eng 65:1871–1884PubMedCrossRef Cherukuri V, Ssenyonga P, Warf BC et al (2018) Learning based segmentation of CT brain images: application to postoperative hydrocephalic scans. IEEE Trans Biomed Eng 65:1871–1884PubMedCrossRef
5.
go back to reference Mahomed N, van Ginneken B, Philipsen RHHM et al (2020) Computer-aided diagnosis for World Health Organization–defined chest radiograph primary-endpoint pneumonia in children. Pediatr Radiol 50:482–491PubMedCrossRef Mahomed N, van Ginneken B, Philipsen RHHM et al (2020) Computer-aided diagnosis for World Health Organization–defined chest radiograph primary-endpoint pneumonia in children. Pediatr Radiol 50:482–491PubMedCrossRef
6.
go back to reference Alqahtani FF, Messina F, Offiah AC (2019) Are semi-automated software program [sic] designed for adults accurate for the identification of vertebral fractures in children? Eur Radiol 29:6780–6789PubMedPubMedCentralCrossRef Alqahtani FF, Messina F, Offiah AC (2019) Are semi-automated software program [sic] designed for adults accurate for the identification of vertebral fractures in children? Eur Radiol 29:6780–6789PubMedPubMedCentralCrossRef
7.
go back to reference Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC (2021) Artificial intelligence in paediatric radiology: future opportunities. Br J Radiol 94:20200975PubMedCrossRef Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC (2021) Artificial intelligence in paediatric radiology: future opportunities. Br J Radiol 94:20200975PubMedCrossRef
8.
go back to reference Benjamens S, Dhunnoo P, Meskó B (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 3:118PubMedPubMedCentralCrossRef Benjamens S, Dhunnoo P, Meskó B (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 3:118PubMedPubMedCentralCrossRef
10.
go back to reference Lin DJ, Johnson PM, Knoll F, Lui YW (2021) Artificial intelligence for MR image reconstruction: an overview for clinicians. J Magn Reson Imaging 53:1015–1028PubMedCrossRef Lin DJ, Johnson PM, Knoll F, Lui YW (2021) Artificial intelligence for MR image reconstruction: an overview for clinicians. J Magn Reson Imaging 53:1015–1028PubMedCrossRef
11.
go back to reference Johnson PM, Drangova M (2019) Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med 82:901–910PubMed Johnson PM, Drangova M (2019) Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med 82:901–910PubMed
12.
go back to reference Wolterink JM, Leiner T, Viergever MA, Isgum I (2017) Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36:2536–2545PubMedCrossRef Wolterink JM, Leiner T, Viergever MA, Isgum I (2017) Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36:2536–2545PubMedCrossRef
13.
go back to reference MacDougall RD, Zhang Y, Callahan MJ et al (2019) Improving low-dose pediatric abdominal CT by using convolutional neural networks. Radiol Artif Intell 1:e180087PubMedPubMedCentralCrossRef MacDougall RD, Zhang Y, Callahan MJ et al (2019) Improving low-dose pediatric abdominal CT by using convolutional neural networks. Radiol Artif Intell 1:e180087PubMedPubMedCentralCrossRef
14.
go back to reference Winkel DJ, Heye T, Weikert TJ et al (2019) Evaluation of an AI-based detection software for acute findings in abdominal computed tomography scans: toward an automated work list prioritization of routine CT examinations. Investig Radiol 54:55–59CrossRef Winkel DJ, Heye T, Weikert TJ et al (2019) Evaluation of an AI-based detection software for acute findings in abdominal computed tomography scans: toward an automated work list prioritization of routine CT examinations. Investig Radiol 54:55–59CrossRef
15.
go back to reference Prevedello LM, Erdal BS, Ryu JL et al (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931PubMedCrossRef Prevedello LM, Erdal BS, Ryu JL et al (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931PubMedCrossRef
16.
go back to reference Halabi SS, Prevedello LM, Kalpathy-Cramer J et al (2019) The RSNA pediatric bone age machine learning challenge. Radiology 290:498–503PubMedCrossRef Halabi SS, Prevedello LM, Kalpathy-Cramer J et al (2019) The RSNA pediatric bone age machine learning challenge. Radiology 290:498–503PubMedCrossRef
17.
go back to reference Larson DB, Chen MC, Lungren MP et al (2018) Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287:313–322PubMedCrossRef Larson DB, Chen MC, Lungren MP et al (2018) Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287:313–322PubMedCrossRef
19.
go back to reference Reddy NE, Rayan JC, Annapragada AV et al (2020) Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists. Pediatr Radiol 50:516–523PubMedCrossRef Reddy NE, Rayan JC, Annapragada AV et al (2020) Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists. Pediatr Radiol 50:516–523PubMedCrossRef
20.
go back to reference Pan I, Baird GL, Mutasa S et al (2020) Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs. Radiol Artif Intell 2:e190198PubMedPubMedCentralCrossRef Pan I, Baird GL, Mutasa S et al (2020) Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs. Radiol Artif Intell 2:e190198PubMedPubMedCentralCrossRef
21.
go back to reference Pan I, Thodberg HH, Halabi SS et al (2019) Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA Machine Learning Challenge. Radiol Artif Intell 1:e190053PubMedPubMedCentralCrossRef Pan I, Thodberg HH, Halabi SS et al (2019) Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA Machine Learning Challenge. Radiol Artif Intell 1:e190053PubMedPubMedCentralCrossRef
22.
go back to reference Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66PubMedCrossRef Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66PubMedCrossRef
23.
go back to reference Yi PH, Kim TK, Wei J et al (2019) Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning. Pediatr Radiol 49:1066–1070PubMedCrossRef Yi PH, Kim TK, Wei J et al (2019) Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning. Pediatr Radiol 49:1066–1070PubMedCrossRef
24.
go back to reference Jeffries BF, Tarlton M, De Smet AA et al (1980) Computerized measurement and analysis of scoliosis: a more accurate representation of the shape of the curve. Radiology 134:381–385PubMedCrossRef Jeffries BF, Tarlton M, De Smet AA et al (1980) Computerized measurement and analysis of scoliosis: a more accurate representation of the shape of the curve. Radiology 134:381–385PubMedCrossRef
25.
go back to reference Horng M-H, Kuok C-P, Fu M-J et al (2019) Cobb angle measurement of spine from X-ray images using convolutional neural network. Comput Math Methods Med 2019:1–18CrossRef Horng M-H, Kuok C-P, Fu M-J et al (2019) Cobb angle measurement of spine from X-ray images using convolutional neural network. Comput Math Methods Med 2019:1–18CrossRef
26.
go back to reference Wu H, Bailey C, Rasoulinejad P, Li S (2018) Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-net. Med Image Anal 48:1–11PubMedCrossRef Wu H, Bailey C, Rasoulinejad P, Li S (2018) Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-net. Med Image Anal 48:1–11PubMedCrossRef
27.
go back to reference Yang J, Zhang K, Fan H et al (2019) Development and validation of deep learning algorithms for scoliosis screening using back images. Commun Biol 2:390PubMedPubMedCentralCrossRef Yang J, Zhang K, Fan H et al (2019) Development and validation of deep learning algorithms for scoliosis screening using back images. Commun Biol 2:390PubMedPubMedCentralCrossRef
28.
go back to reference Zheng Q, Furth SL, Tasian GE, Fan Y (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15:75.e1–75.e7 Zheng Q, Furth SL, Tasian GE, Fan Y (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15:75.e1–75.e7
29.
go back to reference Pilla NI, Rinaldi J, Hatch M, Hennrikus W (2020) Epidemiological analysis of displaced supracondylar fractures. Cureus 12:e7734PubMedPubMedCentral Pilla NI, Rinaldi J, Hatch M, Hennrikus W (2020) Epidemiological analysis of displaced supracondylar fractures. Cureus 12:e7734PubMedPubMedCentral
30.
go back to reference Choi JW, Cho YJ, Lee S et al (2020) Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Investig Radiol 55:101–110CrossRef Choi JW, Cho YJ, Lee S et al (2020) Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Investig Radiol 55:101–110CrossRef
31.
go back to reference Rayan JC, Reddy N, Kan JH et al (2019) Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiol Artif Intell 1:e180015PubMedPubMedCentralCrossRef Rayan JC, Reddy N, Kan JH et al (2019) Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiol Artif Intell 1:e180015PubMedPubMedCentralCrossRef
33.
go back to reference England JR, Gross JS, White EA et al (2018) Detection of traumatic pediatric elbow joint effusion using a deep convolutional neural network. AJR Am J Roentgenol 211:1361–1368PubMedCrossRef England JR, Gross JS, White EA et al (2018) Detection of traumatic pediatric elbow joint effusion using a deep convolutional neural network. AJR Am J Roentgenol 211:1361–1368PubMedCrossRef
34.
go back to reference Banerjee I, Crawley A, Bhethanabotla M et al (2018) Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput Med Imaging Graph 65:167–175PubMedCrossRef Banerjee I, Crawley A, Bhethanabotla M et al (2018) Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput Med Imaging Graph 65:167–175PubMedCrossRef
35.
go back to reference Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Comm ACM 60 Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Comm ACM 60
36.
go back to reference Somasundaram E, Dillman JR, Crotty EJ et al (2020) Automatic detection of inadequate pediatric lateral neck radiographs of the airway and soft tissues using deep learning. Radiol Artif Intell 2:e190226PubMedPubMedCentralCrossRef Somasundaram E, Dillman JR, Crotty EJ et al (2020) Automatic detection of inadequate pediatric lateral neck radiographs of the airway and soft tissues using deep learning. Radiol Artif Intell 2:e190226PubMedPubMedCentralCrossRef
37.
go back to reference The ADHD-200 Consortium (2012) The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:62PubMedCentral The ADHD-200 Consortium (2012) The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:62PubMedCentral
38.
go back to reference Chen M, Li H, Wang J et al (2019) A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell 2:e190012PubMedPubMedCentralCrossRef Chen M, Li H, Wang J et al (2019) A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell 2:e190012PubMedPubMedCentralCrossRef
39.
go back to reference Otjen JP, Stanescu AL, Alessio AM, Parisi MT (2020) Ovarian torsion: developing a machine-learned algorithm for diagnosis. Pediatr Radiol 50:706–714PubMedCrossRef Otjen JP, Stanescu AL, Alessio AM, Parisi MT (2020) Ovarian torsion: developing a machine-learned algorithm for diagnosis. Pediatr Radiol 50:706–714PubMedCrossRef
40.
go back to reference Zucker EJ, Barnes ZA, Lungren MP et al (2020) Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis. J Cyst Fibros 19:131–138PubMedCrossRef Zucker EJ, Barnes ZA, Lungren MP et al (2020) Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis. J Cyst Fibros 19:131–138PubMedCrossRef
41.
go back to reference Li H, He L, Dudley JA et al (2021) DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Pediatr Radiol 51:392–402PubMedCrossRef Li H, He L, Dudley JA et al (2021) DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Pediatr Radiol 51:392–402PubMedCrossRef
42.
go back to reference Kim S, Yoon H, Lee M-J et al (2019) Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children. Sci Rep 9:19420PubMedPubMedCentralCrossRef Kim S, Yoon H, Lee M-J et al (2019) Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children. Sci Rep 9:19420PubMedPubMedCentralCrossRef
44.
go back to reference Shi W, Yan G, Li Y et al (2020) Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty. Neuroimage 223:117316PubMedCrossRef Shi W, Yan G, Li Y et al (2020) Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty. Neuroimage 223:117316PubMedCrossRef
45.
go back to reference Liao L, Zhang X, Zhao F et al (2020) Multi-branch deformable convolutional neural network with label distribution learning for fetal brain age prediction. Presented at the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City Liao L, Zhang X, Zhao F et al (2020) Multi-branch deformable convolutional neural network with label distribution learning for fetal brain age prediction. Presented at the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City
46.
go back to reference Pisapia JM, Akbari H, Rozycki M et al (2018) Use of fetal magnetic resonance image analysis and machine learning to predict the need for postnatal cerebrospinal fluid diversion in fetal ventriculomegaly. JAMA Pediatr 172:128PubMedCrossRef Pisapia JM, Akbari H, Rozycki M et al (2018) Use of fetal magnetic resonance image analysis and machine learning to predict the need for postnatal cerebrospinal fluid diversion in fetal ventriculomegaly. JAMA Pediatr 172:128PubMedCrossRef
47.
go back to reference Attallah O, Sharkas MA, Gadelkarim H (2019) Fetal brain abnormality classification from MRI images of different gestational age. Brain Sci 9:231PubMedCentralCrossRef Attallah O, Sharkas MA, Gadelkarim H (2019) Fetal brain abnormality classification from MRI images of different gestational age. Brain Sci 9:231PubMedCentralCrossRef
48.
go back to reference Li J, Luo Y, Shi L et al (2020) Automatic fetal brain extraction from 2D in utero fetal MRI slices using deep neural network. Neurocomputing 378:335–349CrossRef Li J, Luo Y, Shi L et al (2020) Automatic fetal brain extraction from 2D in utero fetal MRI slices using deep neural network. Neurocomputing 378:335–349CrossRef
49.
go back to reference Wang X, Peng Y, Lu L et al (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Presented at the 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR), Honolulu Wang X, Peng Y, Lu L et al (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Presented at the 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR), Honolulu
54.
go back to reference Yune S, Lee H, Kim M et al (2019) Beyond human perception: sexual dimorphism in hand and wrist radiographs is discernible by a deep learning model. J Digit Imaging 32:665–671PubMedCrossRef Yune S, Lee H, Kim M et al (2019) Beyond human perception: sexual dimorphism in hand and wrist radiographs is discernible by a deep learning model. J Digit Imaging 32:665–671PubMedCrossRef
55.
go back to reference Wagner MW, Bilbily A, Beheshti M et al (2021) Artificial intelligence and radiomics in pediatric molecular imaging. Methods 188:37–43PubMedCrossRef Wagner MW, Bilbily A, Beheshti M et al (2021) Artificial intelligence and radiomics in pediatric molecular imaging. Methods 188:37–43PubMedCrossRef
56.
go back to reference Wang H, Zhang J, Bao S et al (2020) Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: a two-center study. J Magn Reson Imaging 52:873–882PubMedCrossRef Wang H, Zhang J, Bao S et al (2020) Preoperative MRI-based radiomic machine-learning nomogram may accurately distinguish between benign and malignant soft-tissue lesions: a two-center study. J Magn Reson Imaging 52:873–882PubMedCrossRef
57.
go back to reference Liu B, Chi W, Li X et al (2020) Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect. J Cancer Res Clin Oncol 146:153–185PubMedCrossRef Liu B, Chi W, Li X et al (2020) Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect. J Cancer Res Clin Oncol 146:153–185PubMedCrossRef
Metadata
Title
The current and future roles of artificial intelligence in pediatric radiology
Authors
Jeffrey P. Otjen
Michael M. Moore
Erin K. Romberg
Francisco A. Perez
Ramesh S. Iyer
Publication date
27-05-2021
Publisher
Springer Berlin Heidelberg
Published in
Pediatric Radiology / Issue 11/2022
Print ISSN: 0301-0449
Electronic ISSN: 1432-1998
DOI
https://doi.org/10.1007/s00247-021-05086-9

Other articles of this Issue 11/2022

Pediatric Radiology 11/2022 Go to the issue