{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T20:10:45Z","timestamp":1788725445796,"version":"build-2803163510"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"DOI":"10.1007\/s00247-026-06560-y","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:51:25Z","timestamp":1773219085000},"page":"1945-1958","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The role of artificial intelligence in paediatric abdominal imaging"],"prefix":"10.1007","volume":"56","author":[{"given":"Ione","family":"Limantoro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samual","family":"Stafrace","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilze","family":"Apine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmelo","family":"Sofia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seema","family":"Toso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damjana","family":"Kljucevsek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Perucca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"6560_CR1","volume-title":"Oxford dictionary of English","author":"A Stevenson","year":"2010","unstructured":"Stevenson A (2010) Oxford dictionary of English, 3rd ed. Oxford university press, New York","edition":"3rd ed"},{"key":"6560_CR2","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1007\/s00247-024-06098-x","volume":"54","author":"M Straus Takahashi","year":"2024","unstructured":"Straus Takahashi M, Donnelly LF, Siala S (2024) Artificial intelligence: a primer for pediatric radiologists. Pediatr Radiol 54:2127\u20132142","journal-title":"Pediatr Radiol"},{"key":"6560_CR3","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20200975","volume":"94","author":"N Davendralingam","year":"2021","unstructured":"Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC (2021) Artificial intelligence in paediatric radiology: future opportunities. Br J Radiol 94:20200975","journal-title":"Br J Radiol"},{"key":"6560_CR4","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1007\/s00247-021-05057-0","volume":"52","author":"JR Dillman","year":"2022","unstructured":"Dillman JR, Somasundaram E, Brady SL, He L (2022) Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol 52:2139\u20132148","journal-title":"Pediatr Radiol"},{"key":"6560_CR5","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1007\/s00247-021-05129-1","volume":"52","author":"R Meshaka","year":"2022","unstructured":"Meshaka R, Pinto Dos Santos D, Arthurs OJ et al (2022) Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know. Pediatr Radiol 52:2101\u20132110","journal-title":"Pediatr Radiol"},{"key":"6560_CR6","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s00247-021-05086-9","volume":"52","author":"JP Otjen","year":"2022","unstructured":"Otjen JP, Moore MM, Romberg EK et al (2022) The current and future roles of artificial intelligence in pediatric radiology. Pediatr Radiol 52:2065\u20132073","journal-title":"Pediatr Radiol"},{"key":"6560_CR7","doi-asserted-by":"publisher","unstructured":"Jordan P, Adamson PM, Bhattbhatt V et al (2021) Pediatric chest\/abdomen\/pelvic CT exams with expert organ contours (Pediatric-CT-SEG). Med Phys 49:3523\u20133528. https:\/\/doi.org\/10.1002\/mp.15485.","DOI":"10.1002\/mp.15485"},{"key":"6560_CR8","doi-asserted-by":"publisher","unstructured":"Erdman L, Heller N, He P et al (2025) Lack of children in public medical imaging data points to growing age bias in biomedical AI. Res Sq rs.3.rs-7713694. https:\/\/doi.org\/10.21203\/rs.3.rs-7713694\/v1","DOI":"10.21203\/rs.3.rs-7713694\/v1"},{"key":"6560_CR9","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s00247-021-05195-5","volume":"52","author":"SC Shelmerdine","year":"2022","unstructured":"Shelmerdine SC, Rosendahl K, Arthurs OJ (2022) Artificial intelligence in paediatric radiology: international survey of health care professionals\u2019 opinions. Pediatr Radiol 52:30\u201341","journal-title":"Pediatr Radiol"},{"key":"6560_CR10","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1007\/s11604-025-01775-9","volume":"43","author":"S Ode","year":"2025","unstructured":"Ode S, Fujikawa A, Hiroishi A et al (2025) Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation. Jpn J Radiol 43:1365\u20131371","journal-title":"Jpn J Radiol"},{"key":"6560_CR11","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1007\/s00247-014-3235-2","volume":"45","author":"RDA Khawaja","year":"2015","unstructured":"Khawaja RDA, Singh S, Otrakji A et al (2015) Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms. Pediatr Radiol 45:1046\u20131055","journal-title":"Pediatr Radiol"},{"key":"6560_CR12","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1148\/radiol.13130362","volume":"270","author":"EA Smith","year":"2014","unstructured":"Smith EA, Dillman JR, Goodsitt MM et al (2014) Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. Radiology 270:526\u2013534","journal-title":"Radiology"},{"key":"6560_CR13","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1148\/radiol.13122578","volume":"270","author":"SL Brady","year":"2014","unstructured":"Brady SL, Moore BM, Yee BS, Kaufman RA (2014) Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise. Radiology 270:223\u2013231","journal-title":"Radiology"},{"key":"6560_CR14","volume":"1","author":"RD MacDougall","year":"2019","unstructured":"MacDougall RD, Zhang Y, Callahan MJ et al (2019) Improving low-dose pediatric abdominal CT by using convolutional neural networks. Radiology: Artificial Intelligence 1:e180087","journal-title":"Radiology: Artificial Intelligence"},{"key":"6560_CR15","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2021.0466","volume":"23","author":"W Son","year":"2022","unstructured":"Son W, Kim M, Hwang J-Y et al (2022) Comparison of a deep learning-based reconstruction algorithm with filtered back projection and iterative reconstruction algorithms for pediatric abdominopelvic CT. Korean J Radiol 23:752","journal-title":"Korean J Radiol"},{"key":"6560_CR16","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1148\/radiol.2020202317","volume":"298","author":"SL Brady","year":"2021","unstructured":"Brady SL, Trout AT, Somasundaram E et al (2021) Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology 298:180\u2013188","journal-title":"Radiology"},{"key":"6560_CR17","first-page":"177","volume":"30","author":"J Sun","year":"2022","unstructured":"Sun J, Li H, Li H et al (2022) Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis. J X-Ray Sci Technol 30:177\u2013184","journal-title":"J X-Ray Sci Technol"},{"key":"6560_CR18","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1007\/s00247-024-05953-1","volume":"54","author":"D-J Shin","year":"2024","unstructured":"Shin D-J, Choi YH, Lee SB et al (2024) Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography. Pediatr Radiol 54:1315\u20131324","journal-title":"Pediatr Radiol"},{"key":"6560_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111434","volume":"175","author":"MAK Brix","year":"2024","unstructured":"Brix MAK, J\u00e4rvinen J, Bode MK et al (2024) Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine. Eur J Radiol 175:111434","journal-title":"Eur J Radiol"},{"key":"6560_CR20","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1002\/jmri.29750","volume":"62","author":"L Feng","year":"2025","unstructured":"Feng L, Chandarana H (2025) Accelerated abdominal MRI: a review of current methods and applications. J Magn Reson Imaging 62:654\u2013672","journal-title":"J Magn Reson Imaging"},{"key":"6560_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-025-06313-3","author":"EJ Zucker","year":"2025","unstructured":"Zucker EJ, Milshteyn E, Machado-Rivas FA et al (2025) Deep learning reconstruction for improving image quality of pediatric abdomen MRI using a 3D T1 fast spoiled gradient echo acquisition. Pediatr Radiol. https:\/\/doi.org\/10.1007\/s00247-025-06313-3","journal-title":"Pediatr Radiol"},{"key":"6560_CR22","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.radonc.2020.09.056","volume":"153","author":"MC Florkow","year":"2020","unstructured":"Florkow MC, Guerreiro F, Zijlstra F et al (2020) Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours. Radiother Oncol 153:220\u2013227","journal-title":"Radiother Oncol"},{"key":"6560_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00680","volume":"10","author":"S-H Zhen","year":"2020","unstructured":"Zhen S-H, Cheng M, Tao Y-B et al (2020) Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol 10:680","journal-title":"Front Oncol"},{"key":"6560_CR24","doi-asserted-by":"publisher","first-page":"4365","DOI":"10.1007\/s00431-023-05113-x","volume":"182","author":"Y Yang","year":"2023","unstructured":"Yang Y, Zhou Z, Li Y (2023) MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy. Eur J Pediatr 182:4365\u20134368","journal-title":"Eur J Pediatr"},{"key":"6560_CR25","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.644994","volume":"11","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Sun J, Xia Y et al (2021) Preoperative assessment for event-free survival with hepatoblastoma in pediatric patients by developing a CT-based radiomics model. Front Oncol 11:644994","journal-title":"Front Oncol"},{"key":"6560_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-024-05746-x","volume":"150","author":"Y Yang","year":"2024","unstructured":"Yang Y, Wang H, Si J et al (2024) Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics. J Cancer Res Clin Oncol 150:223","journal-title":"J Cancer Res Clin Oncol"},{"key":"6560_CR27","doi-asserted-by":"publisher","first-page":"5056","DOI":"10.1007\/s00330-023-10495-5","volume":"34","author":"M Gross","year":"2024","unstructured":"Gross M, Huber S, Arora S et al (2024) Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. Eur Radiol 34:5056\u20135065","journal-title":"Eur Radiol"},{"key":"6560_CR28","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s00247-020-04854-3","volume":"51","author":"H Li","year":"2021","unstructured":"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\u2013402","journal-title":"Pediatr Radiol"},{"key":"6560_CR29","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.1007\/s00330-024-11312-3","volume":"35","author":"R Ali","year":"2025","unstructured":"Ali R, Li H, Zhang H et al (2025) Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI. Eur Radiol 35:4362\u20134373","journal-title":"Eur Radiol"},{"key":"6560_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01362-w","author":"H Zhang","year":"2024","unstructured":"Zhang H, Li H, Ali R et al (2024) Development and validation of a modality-invariant 3D Swin U-Net transformer for liver and spleen segmentation on multi-site clinical bi-parametric MR images. J Imaging Inform Med. https:\/\/doi.org\/10.1007\/s10278-024-01362-w","journal-title":"J Imaging Inform Med"},{"key":"6560_CR31","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1016\/j.ijrobp.2024.01.201","volume":"119","author":"K Kumar","year":"2024","unstructured":"Kumar K, Yeo AU, McIntosh L et al (2024) Deep learning auto-segmentation network for pediatric computed tomography data sets: can we extrapolate from adults? Int J Radiat Oncol Biol Phys 119:1297\u20131306","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"6560_CR32","doi-asserted-by":"publisher","first-page":"e2430931","DOI":"10.2214\/ajr.24.30931","volume":"223","author":"E Somasundaram","year":"2024","unstructured":"Somasundaram E, Taylor Z, Alves VV et al (2024) Deep learning models for abdominal CT organ segmentation in children: development and validation in internal and heterogeneous public datasets. AJR Am J Roentgenol 223:e2430931","journal-title":"AJR Am J Roentgenol"},{"key":"6560_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.pan.2025.06.006","author":"E Keles","year":"2025","unstructured":"Keles E, Yazol M, Durak G et al (2025) Pediatric pancreas segmentation from MRI scans with deep learning. Pancreatology. https:\/\/doi.org\/10.1016\/j.pan.2025.06.006","journal-title":"Pancreatology"},{"key":"6560_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-025-04990-0","author":"A Gupta","year":"2025","unstructured":"Gupta A, Rajamohan N, Bansal B et al (2025) Applications of artificial intelligence in abdominal imaging. Abdom Radiol. https:\/\/doi.org\/10.1007\/s00261-025-04990-0","journal-title":"Abdom Radiol"},{"key":"6560_CR35","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1002\/bdrc.21084","volume":"102","author":"ACS dos Santos Junior","year":"2014","unstructured":"dos Santos Junior ACS, de Miranda DM, Sim\u00f5es e Silva AC (2014) Congenital anomalies of the kidney and urinary tract: an embryogenetic review. Birth Defects Res C Embryo Today Rev 102:374\u2013381","journal-title":"Birth Defects Res C Embryo Today Rev"},{"key":"6560_CR36","doi-asserted-by":"publisher","first-page":"75.e1","DOI":"10.1016\/j.jpurol.2018.10.020","volume":"15","author":"Q Zheng","year":"2019","unstructured":"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","journal-title":"J Pediatr Urol"},{"key":"6560_CR37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.urology.2020.05.019","volume":"142","author":"S Yin","year":"2020","unstructured":"Yin S, Peng Q, Li H et al (2020) Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology 142:183\u2013189","journal-title":"Urology"},{"key":"6560_CR38","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1007\/s00247-025-06311-5","volume":"55","author":"K Chung","year":"2025","unstructured":"Chung K, Wu S, Jeanne C, Tsai A (2025) Comparison of neural networks for classification of urinary tract dilation from renal ultrasounds: evaluation of agreement with expert categorization. Pediatr Radiol 55:1846\u20131856","journal-title":"Pediatr Radiol"},{"key":"6560_CR39","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1038\/s41390-023-02553-x","volume":"94","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Li H, Huang Y et al (2023) CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center. Pediatr Res 94:1104\u20131110","journal-title":"Pediatr Res"},{"key":"6560_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s12887-024-04775-2","volume":"24","author":"W Li","year":"2024","unstructured":"Li W, Sun Y, Zhang G et al (2024) Automated segmentation and volume prediction in pediatric Wilms\u2019 tumor CT using nnu-net. BMC Pediatr 24:321","journal-title":"BMC Pediatr"},{"key":"6560_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-024-00729-0","volume":"24","author":"O Hild","year":"2024","unstructured":"Hild O, Berriet P, Nallet J et al (2024) Automation of Wilms\u2019 tumor segmentation by artificial intelligence. Cancer Imaging 24:83","journal-title":"Cancer Imaging"},{"key":"6560_CR42","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s00467-022-05677-0","volume":"38","author":"JK Weaver","year":"2023","unstructured":"Weaver JK, Milford K, Rickard M et al (2023) Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves. Pediatr Nephrol 38:839\u2013846","journal-title":"Pediatr Nephrol"},{"key":"6560_CR43","doi-asserted-by":"publisher","first-page":"514.e1","DOI":"10.1016\/j.jpurol.2022.12.017","volume":"19","author":"JK Weaver","year":"2023","unstructured":"Weaver JK, Logan J, Broms R et al (2023) Deep learning of renal scans in children with antenatal hydronephrosis. J Pediatr Urol 19:514.e1-514.e7","journal-title":"J Pediatr Urol"},{"key":"6560_CR44","doi-asserted-by":"crossref","unstructured":"Haghighi M, Warfield SK, Kurugol S (2017) Automatic renal segmentation in DCE-MRI using convolutional neural networks","DOI":"10.1109\/ISBI.2018.8363865"},{"key":"6560_CR45","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.ekir.2023.10.029","volume":"9","author":"J Taylor","year":"2024","unstructured":"Taylor J, Thomas R, Metherall P et al (2024) An artificial intelligence generated automated algorithm to measure total kidney volume in ADPKD. Kidney Int Rep 9:249\u2013256","journal-title":"Kidney Int Rep"},{"key":"6560_CR46","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s00247-009-1536-7","volume":"40","author":"P-H Vivier","year":"2010","unstructured":"Vivier P-H, Dolores M, Taylor M, Dacher J-N (2010) MR urography in children. Part 2: how to use ImageJ MR urography processing software. Pediatr Radiol 40:739\u2013746","journal-title":"Pediatr Radiol"},{"key":"6560_CR47","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/s00247-009-1458-4","volume":"40","author":"D Khrichenko","year":"2010","unstructured":"Khrichenko D, Darge K (2010) Functional analysis in MR urography \u2014 made simple. Pediatr Radiol 40:182\u2013199","journal-title":"Pediatr Radiol"},{"key":"6560_CR48","doi-asserted-by":"publisher","unstructured":"(2025) ESPR 2025. Pediatr Radiol 55:175\u2013284. https:\/\/doi.org\/10.1007\/s00247-025-06244-z","DOI":"10.1007\/s00247-025-06244-z"},{"key":"6560_CR49","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.diii.2022.09.003","volume":"104","author":"M Barat","year":"2023","unstructured":"Barat M, Gaillard M, Cottereau A-S et al (2023) Artificial intelligence in adrenal imaging: a critical review of current applications. Diagn Interv Imaging 104:37\u201342","journal-title":"Diagn Interv Imaging"},{"key":"6560_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2025.104911","volume":"130","author":"Y Salimi","year":"2025","unstructured":"Salimi Y, Shiri I, Mansouri Z, Zaidi H (2025) Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images. Phys Med 130:104911","journal-title":"Phys Med"},{"key":"6560_CR51","doi-asserted-by":"publisher","first-page":"5016","DOI":"10.1007\/s00330-024-10589-8","volume":"34","author":"IO Koska","year":"2024","unstructured":"Koska IO, Ozcan HN, Tan AA et al (2024) Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children. Eur Radiol 34:5016\u20135027","journal-title":"Eur Radiol"},{"key":"6560_CR52","doi-asserted-by":"publisher","DOI":"10.1093\/ecco-jcc\/jjaf037","volume":"19","author":"L Kumaralingam","year":"2025","unstructured":"Kumaralingam L, Le May K, Dang VB et al (2025) Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images. J Crohns Colitis 19:jjaf037","journal-title":"J Crohns Colitis"},{"key":"6560_CR53","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1002\/jmri.26330","volume":"49","author":"Y Lamash","year":"2019","unstructured":"Lamash Y, Kurugol S, Freiman M et al (2019) Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn\u2019s disease from MRI. J Magn Reson Imaging 49:1565\u20131576","journal-title":"J Magn Reson Imaging"},{"key":"6560_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-55536-6","volume":"9","author":"S Kim","year":"2019","unstructured":"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 9:19420","journal-title":"Sci Rep"},{"key":"6560_CR55","doi-asserted-by":"publisher","DOI":"10.2196\/72097","volume":"27","author":"JH Lee","year":"2025","unstructured":"Lee JH, Kim PH, Son N-H et al (2025) External validation of an upgraded AI model for screening ileocolic intussusception using pediatric abdominal radiographs: multicenter retrospective study. J Med Internet Res 27:e72097","journal-title":"J Med Internet Res"},{"key":"6560_CR56","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9285238","volume":"2022","author":"Z Li","year":"2022","unstructured":"Li Z, Song C, Huang J et al (2022) Performance of deep learning-based algorithm for detection of pediatric intussusception on abdominal ultrasound images. Gastroenterol Res Pract 2022:9285238","journal-title":"Gastroenterol Res Pract"},{"key":"6560_CR57","doi-asserted-by":"publisher","DOI":"10.4274\/dir.2025.242950","author":"E Ayaz","year":"2025","unstructured":"Ayaz E, G\u00fc\u00e7l\u00fc H, Oktay AB (2025) Diagnostic accuracy of convolutional neural network algorithms to distinguish gastrointestinal obstruction on conventional radiographs in a pediatric population. Diagn Interv Radiol Ank Turk. https:\/\/doi.org\/10.4274\/dir.2025.242950","journal-title":"Diagn Interv Radiol Ank Turk"},{"key":"6560_CR58","doi-asserted-by":"publisher","first-page":"51050","DOI":"10.1109\/ACCESS.2021.3069191","volume":"9","author":"W Gao","year":"2021","unstructured":"Gao W, Pei Y, Liang H et al (2021) Multimodal AI system for the rapid diagnosis and surgical prediction of necrotizing enterocolitis. IEEE Access 9:51050\u201351064","journal-title":"IEEE Access"},{"key":"6560_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpedsurg.2024.06.001","volume":"59","author":"JH Weller","year":"2024","unstructured":"Weller JH, Scheese D, Tragesser C et al (2024) Artificial intelligence vs. doctors: diagnosing necrotizing enterocolitis on abdominal radiographs. J Pediatr Surg 59:161592","journal-title":"J Pediatr Surg"},{"key":"6560_CR60","doi-asserted-by":"publisher","first-page":"e2299","DOI":"10.1055\/s-0043-1771051","volume":"41","author":"RDE Henderson","year":"2024","unstructured":"Henderson RDE, Padash S, Adams SJ et al (2024) Neonatal catheter and tube placement and radiographic assessment statistics in relation to important anatomic landmarks. Am J Perinatol 41:e2299\u2013e2306","journal-title":"Am J Perinatol"},{"key":"6560_CR61","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190082","volume":"2","author":"X Yi","year":"2020","unstructured":"Yi X, Adams SJ, Henderson RDE, Babyn P (2020) Computer-aided assessment of catheters and tubes on radiographs: how good is artificial intelligence for assessment? Radiol Artif Intell 2:e190082","journal-title":"Radiol Artif Intell"},{"key":"6560_CR62","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1007\/s10278-021-00473-y","volume":"34","author":"RDE Henderson","year":"2021","unstructured":"Henderson RDE, Yi X, Adams SJ, Babyn P (2021) Automatic detection and classification of multiple catheters in neonatal radiographs with deep learning. J Digit Imaging 34:888\u2013897","journal-title":"J Digit Imaging"},{"key":"6560_CR63","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1038\/s41390-022-02226-1","volume":"93","author":"S Ramgopal","year":"2023","unstructured":"Ramgopal S, Sanchez-Pinto LN, Horvat CM et al (2023) Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res 93:334\u2013341","journal-title":"Pediatr Res"},{"key":"6560_CR64","doi-asserted-by":"publisher","first-page":"e128","DOI":"10.1097\/TA.0000000000002906","volume":"90","author":"S Ramgopal","year":"2021","unstructured":"Ramgopal S, Horvat CM (2021) Machine learning approaches for the identification of children at low risk of intra-abdominal injury requiring intervention. J Trauma Acute Care Surg 90:e128\u2013e129","journal-title":"J Trauma Acute Care Surg"},{"key":"6560_CR65","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1097\/TA.0000000000002717","volume":"89","author":"C Pennell","year":"2020","unstructured":"Pennell C, Polet C, Arthur LG et al (2020) Risk assessment for intra-abdominal injury following blunt trauma in children: derivation and validation of a machine learning model. J Trauma Acute Care Surg 89:153\u2013159","journal-title":"J Trauma Acute Care Surg"},{"key":"6560_CR66","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.jpedsurg.2020.10.021","volume":"56","author":"N Shahi","year":"2021","unstructured":"Shahi N, Shahi AK, Phillips R et al (2021) Decision-making in pediatric blunt solid organ injury: a deep learning approach to predict massive transfusion, need for operative management, and mortality risk. J Pediatr Surg 56:379\u2013384","journal-title":"J Pediatr Surg"},{"key":"6560_CR67","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2025.1522845","volume":"13","author":"A Deleon","year":"2025","unstructured":"Deleon A, Murala A, Decker I et al (2025) Machine learning-based prediction of mortality in pediatric trauma patients. Front Pediatr 13:1522845","journal-title":"Front Pediatr"},{"key":"6560_CR68","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1002\/jum.15868","volume":"41","author":"AE Kornblith","year":"2022","unstructured":"Kornblith AE, Addo N, Dong R et al (2022) Development and validation of a deep learning strategy for automated view classification of pediatric focused assessment with sonography for trauma. J Ultrasound Med 41:1915\u20131924","journal-title":"J Ultrasound Med"},{"key":"6560_CR69","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1097\/TA.0000000000004425","volume":"98","author":"J Cull","year":"2025","unstructured":"Cull J, Morrow D, Manasco C et al (2025) A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence. J Trauma Acute Care Surg 98:111\u2013116","journal-title":"J Trauma Acute Care Surg"},{"key":"6560_CR70","doi-asserted-by":"publisher","DOI":"10.1186\/s40001-022-00943-1","volume":"27","author":"S Huang","year":"2022","unstructured":"Huang S, Zhou Z, Qian X et al (2022) Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry. Eur J Med Res 27:305","journal-title":"Eur J Med Res"},{"key":"6560_CR71","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.1148\/rg.2017170026","volume":"37","author":"A Sintim-Damoa","year":"2017","unstructured":"Sintim-Damoa A, Majmudar AS, Cohen HL, Parvey LS (2017) Pediatric ovarian torsion: spectrum of imaging findings. Radiographics 37:1892\u20131908","journal-title":"Radiographics"},{"key":"6560_CR72","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1007\/s00247-019-04601-3","volume":"50","author":"JP Otjen","year":"2020","unstructured":"Otjen JP, Stanescu AL, Alessio AM, Parisi MT (2020) Ovarian torsion: developing a machine-learned algorithm for diagnosis. Pediatr Radiol 50:706\u2013714","journal-title":"Pediatr Radiol"},{"key":"6560_CR73","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.jpedsurg.2020.01.040","volume":"55","author":"S Dhatt","year":"2020","unstructured":"Dhatt S, Sabhaney V, Bray H, Skarsgard ED (2020) Improving the diagnostic accuracy of appendicitis using a multidisciplinary pathway. J Pediatr Surg 55:889\u2013892","journal-title":"J Pediatr Surg"},{"key":"6560_CR74","doi-asserted-by":"publisher","DOI":"10.3390\/children12040498","volume":"12","author":"M Duci","year":"2025","unstructured":"Duci M, Verlato G, Moschino L et al (2025) Advances in artificial intelligence and machine learning for precision medicine in necrotizing enterocolitis and neonatal sepsis: a state-of-the-art review. Children 12:498","journal-title":"Children"},{"key":"6560_CR75","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s00247-023-05746-y","volume":"54","author":"P Ciet","year":"2024","unstructured":"Ciet P, Eade C, Ho M-L et al (2024) The unintended consequences of artificial intelligence in paediatric radiology. Pediatr Radiol 54:585\u2013593","journal-title":"Pediatr Radiol"},{"key":"6560_CR76","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1007\/s00247-022-05426-3","volume":"53","author":"LB Laborie","year":"2022","unstructured":"Laborie LB, Naidoo J, Pace E et al (2022) European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age. Pediatr Radiol 53:576\u2013580","journal-title":"Pediatr Radiol"}],"container-title":["Pediatric Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06560-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00247-026-06560-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06560-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T20:02:08Z","timestamp":1788724928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00247-026-06560-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":76,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["6560"],"URL":"https:\/\/doi.org\/10.1007\/s00247-026-06560-y","relation":{},"ISSN":["1432-1998"],"issn-type":[{"value":"1432-1998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"9 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"None.","order":1,"name":"Ethics","label":"Conflicts of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}