{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T03:06:41Z","timestamp":1792292801784,"version":"4.4.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01482-x","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T17:31:16Z","timestamp":1742405476000},"page":"3868-3885","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automated Detection of Hydrocephalus in Pediatric Head Computed Tomography Using VGG 16 CNN Deep Learning Architecture and Based Automated Segmentation Workflow for Ventricular Volume Estimation"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1530-4837","authenticated-orcid":false,"given":"Hamza","family":"Sekkat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdellah","family":"Khallouqi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar El","family":"Rhazouani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdellah","family":"Halimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"issue":"4","key":"1482_CR1","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.3171\/2017.10.JNS17439","volume":"130","author":"MC Dewan","year":"2019","unstructured":"Dewan MC, Rattani A, Mekary R, Glancz LJ, Yunusa I, Baticulon RE, Fieggen G, Wellons JC, Park KB, Warf BC. Global hydrocephalus epidemiology and incidence: systematic review and meta-analysis. J Neurosurg. 2019;130(4):1065-1079. https:\/\/doi.org\/10.3171\/2017.10.JNS17439.","journal-title":"J Neurosurg."},{"issue":"8","key":"1482_CR2","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.mpsur.2015.05.006","volume":"33","author":"AK Toma","year":"2015","unstructured":"Toma AK. Hydrocephalus. Surgery (Oxford). 2015;33(8):384-389. https:\/\/doi.org\/10.1016\/j.mpsur.2015.05.006.","journal-title":"Surgery (Oxford)."},{"issue":"7","key":"1482_CR3","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1007\/s10278-022-00657-0","volume":"35","author":"M El Mansouri","year":"2022","unstructured":"El Mansouri M, Choukri A, Semghouli S, Talbi M, Eddaoui K, Saga Z. Size-Specific Dose Estimates for Thoracic and Abdominal Computed Tomography Examinations at Two Moroccan Hospitals. J Digit Imaging. 2022;35(7):1648-1653. https:\/\/doi.org\/10.1007\/s10278-022-00657-0.","journal-title":"J Digit Imaging."},{"issue":"8","key":"1482_CR4","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1007\/s10278-023-00808-x","volume":"36","author":"M Nagata","year":"2023","unstructured":"Nagata M, Ichikawa Y, Domae K, Yoshikawa K, Kanii Y, Yamazaki A, Nagasawa N, Ishida M, Sakuma H. Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method. J Digit Imaging. 2023;36(8):1578-1587. https:\/\/doi.org\/10.1007\/s10278-023-00808-x.","journal-title":"J Digit Imaging."},{"key":"1482_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2024.112214","volume":"226","author":"Y Madkouri","year":"2025","unstructured":"Madkouri Y, Sekkat H, El Merabet Y, Talbi M, Aggour M, Bentayeb F, Khallouqi A. Helical to wide volume acquisition in lumbar spine CT: Structures scoring based analysis. Radiat Phys Chem. 2025;226:112214. https:\/\/doi.org\/10.1016\/j.radphyschem.2024.112214.","journal-title":"Radiat Phys Chem."},{"key":"1482_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2024.112079","volume":"224","author":"A Khallouqi","year":"2024","unstructured":"Khallouqi A, Sekkat H, Halimi A, El Rhazouani O. A closer look at utilized radiation doses during chest CT for COVID-19 patients. Radiat Phys Chem. 2024;224:112079. https:\/\/doi.org\/10.1016\/j.radphyschem.2024.112079.","journal-title":"Radiat Phys Chem."},{"issue":"4","key":"1482_CR7","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1227\/NEU.0b013e318208f5e0","volume":"68","author":"AK Toma","year":"2011","unstructured":"Toma AK, Holl E, Kitchen ND, Watkins LD. Evans\u2019 Index Revisited: The Need for an Alternative in Normal Pressure Hydrocephalus. Neurosurgery. 2011;68(4):939-944. https:\/\/doi.org\/10.1227\/NEU.0b013e318208f5e0.","journal-title":"Neurosurgery."},{"key":"1482_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.4103\/2152-7806.154777","volume":"6","author":"K Reinard","year":"2015","unstructured":"Reinard K, Basheer A, Phillips S, Snyder A, Agarwal A, Jafari-Khouzani K, Soltanian-Zadeh H, Schultz L, Aho T, Schwalb J. Simple and reproducible linear measurements to determine ventricular enlargement in adults. Surg Neurol Int. 2015;6:59. https:\/\/doi.org\/10.4103\/2152-7806.154777.","journal-title":"Surg Neurol Int."},{"issue":"9","key":"1482_CR9","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323-1341. https:\/\/doi.org\/10.1016\/j.mri.2012.05.001.","journal-title":"Magn Reson Imaging."},{"issue":"Suppl 1","key":"1482_CR10","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1472-6947-9-S1-S4","volume":"9","author":"W Chen","year":"2009","unstructured":"Chen W, Smith R, Ji SY, Ward KR, Najarian K. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Mak. 2009;9(Suppl 1):S4. https:\/\/doi.org\/10.1186\/1472-6947-9-S1-S4.","journal-title":"BMC Med Inform Decis Mak."},{"key":"1482_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/8690892","volume":"2017","author":"X Qian","year":"2017","unstructured":"Qian X, Lin Y, Zhao Y, Yue X, Lu B, Wang J. Objective ventricle segmentation in brain CT with ischemic stroke based on anatomical knowledge. Biomed Res Int. 2017;2017:1-11. https:\/\/doi.org\/10.1155\/2017\/8690892.","journal-title":"Biomed Res Int."},{"issue":"2","key":"1482_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1167\/tvst.9.2.14","volume":"9","author":"RY Choi","year":"2020","unstructured":"Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):14. https:\/\/doi.org\/10.1167\/tvst.9.2.14.","journal-title":"Transl Vis Sci Technol."},{"key":"1482_CR13","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: A review. Neurocomputing. 2016;187:27-48. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116.","journal-title":"Neurocomputing."},{"issue":"11","key":"1482_CR14","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.1016\/j.acra.2018.02.018","volume":"25","author":"MP McBee","year":"2018","unstructured":"McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, Tridandapani S, Auffermann WF. Deep learning in radiology. Acad Radiol. 2018;25(11):1472-1480. https:\/\/doi.org\/10.1016\/j.acra.2018.02.018.","journal-title":"Acad Radiol."},{"key":"1482_CR15","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s00247-014-3045-6","volume":"44","author":"MM Rehani","year":"2014","unstructured":"Rehani MM. CT imaging in a large part of the world: What we know and what we can learn. Pediatr Radiol. 2014;44:511-514. https:\/\/doi.org\/10.1007\/s00247-014-3045-6.","journal-title":"Pediatr Radiol."},{"key":"1482_CR16","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27\u201330; Las Vegas, NV. p. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1482_CR17","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015 Jun 7\u201312; Boston, MA, USA. p. 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1482_CR18","doi-asserted-by":"publisher","unstructured":"Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML); 2019 Jun 9\u201315; Long Beach, CA, USA. PMLR 97:6105\u20136114. https:\/\/doi.org\/10.48550\/arXiv.1905.11946.","DOI":"10.48550\/arXiv.1905.11946"},{"key":"1482_CR19","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR); 2015 May 7\u20139; San Diego, CA, USA. https:\/\/doi.org\/10.48550\/arXiv.1409.1556.","DOI":"10.48550\/arXiv.1409.1556"},{"issue":"5","key":"1482_CR20","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J. Convolutional neural networks for medical image analysis: Full training or fine-tuning? IEEE Trans Med Imaging. 2016;35(5):1299-1312. https:\/\/doi.org\/10.1109\/TMI.2016.2535302.","journal-title":"IEEE Trans Med Imaging."},{"key":"1482_CR21","doi-asserted-by":"publisher","unstructured":"Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: Efficient convolutional neural networks for mobile vision applications. 2017. https:\/\/doi.org\/10.48550\/arXiv.1704.04861.","DOI":"10.48550\/arXiv.1704.04861"},{"key":"1482_CR22","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth 16x16 words: Transformers for image recognition at scale. 2020. https:\/\/doi.org\/10.48550\/arXiv.2010.11929.","DOI":"10.48550\/arXiv.2010.11929"},{"key":"1482_CR23","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453","volume":"2","author":"S Van Der Walt","year":"2014","unstructured":"Van Der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T. scikit-image: Image processing in Python. PeerJ. 2014;2:e453. https:\/\/doi.org\/10.7717\/peerj.453.","journal-title":"PeerJ."},{"key":"1482_CR24","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.3174\/ajnr.A6620","volume":"41","author":"J Neikter","year":"2020","unstructured":"Neikter J, Agerskov S, Hellstr\u00f6m P, Tullberg M, Starck G, Ziegelitz D, Farahmand D. Ventricular volume is more strongly associated with clinical improvement than the Evans index after shunting in idiopathic normal pressure hydrocephalus. AJNR Am J Neuroradiol. 2020;41:1187-1192. https:\/\/doi.org\/10.3174\/ajnr.A6620.","journal-title":"AJNR Am J Neuroradiol."},{"issue":"5","key":"1482_CR25","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.3390\/biomedicines11051265","volume":"11","author":"E-S Pyrgelis","year":"2023","unstructured":"Pyrgelis E-S, Velonakis G, Papageorgiou SG, Stefanis L, Kapaki E, Constantinides VC. Imaging markers for normal pressure hydrocephalus: An overview. Biomedicines. 2023;11(5):1265. https:\/\/doi.org\/10.3390\/biomedicines11051265.","journal-title":"Biomedicines."},{"key":"1482_CR26","doi-asserted-by":"publisher","unstructured":"Gebest M, Weiss C, Cho C, Hausner L, Fr\u00f6lich L, Foerster A, Santhanam N, Fontana J, Groden C, Wenz H, Maros M. Longitudinal automated brain volumetry vs. expert visual assessment of atrophy progression on MRI is robust but caution is advised. 2024. https:\/\/doi.org\/10.1101\/2024.05.21.24306349.","DOI":"10.1101\/2024.05.21.24306349"},{"key":"1482_CR27","doi-asserted-by":"publisher","DOI":"10.1088\/1402-4896\/ad8b86","volume":"99","author":"H Sekkat","year":"2024","unstructured":"Sekkat H, Khallouqi A, Madkouri Y, El Atifi W, Rhouch I, El Rhazouani O, Tahiri Z, Talbi M, Halimi A. Development of a novel automated algorithm for patient dosimetry in computed tomography: A step towards the facilitation of size-specific dose estimates and organ dosimetry estimations in a busy clinical workflow. Phys Scr. 2024;99:125012. https:\/\/doi.org\/10.1088\/1402-4896\/ad8b86.","journal-title":"Phys Scr."},{"key":"1482_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2024.112388","volume":"227","author":"H Sekkat","year":"2025","unstructured":"Sekkat H, Khallouqi A, El Rhazouani O, Halimi A, Madkouri Y. HeadCTDosi: A Python-based automated calculation of head water equivalent diameter and size-specific dose estimate based on AAPM 220 report. Radiat Phys Chem. 2025;227:112388. https:\/\/doi.org\/10.1016\/j.radphyschem.2024.112388.","journal-title":"Radiat Phys Chem."},{"key":"1482_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2022.102705","volume":"123","author":"SAA Rub","year":"2023","unstructured":"Rub SAA, Alaiad A, Hmeidi I, Quwaider M, Alzoubi O. Hydrocephalus classification in brain computed tomography medical images using deep learning. Simul Model Pract Theory. 2023;123:102705. https:\/\/doi.org\/10.1016\/j.simpat.2022.102705.","journal-title":"Simul Model Pract Theory."},{"key":"1482_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger K. Densely Connected Convolutional Networks. 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.243.","journal-title":"Networks."},{"key":"1482_CR31","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3171\/2020.6.PEDS20251","volume":"27","author":"JL Quon","year":"2021","unstructured":"Quon JL, Han M, Kim LH, Koran ME, Chen LC, Lee EH, Wright J, Ramaswamy V, Lober RM, Taylor MD, Grant GA, Cheshier SH, Kestle JRW, Edwards MSB, Yeom KW. Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: A clinical tool for the evaluation of pediatric hydrocephalus. J Neurosurg Pediatr. 2021;27:131-138. https:\/\/doi.org\/10.3171\/2020.6.PEDS20251.","journal-title":"J Neurosurg Pediatr."},{"key":"1482_CR32","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1007\/s10278-022-00654-3","volume":"35","author":"Y Huang","year":"2022","unstructured":"Huang Y, Moreno R, Malani R, Meng A, Swinburne N, Holodny AI, Choi Y, Rusinek H, Golomb JB, George A, Parra LC, Young RJ. Deep learning achieves neuroradiologist-level performance in detecting hydrocephalus requiring treatment. J Digit Imaging. 2022;35:1662-1672. https:\/\/doi.org\/10.1007\/s10278-022-00654-3.","journal-title":"J Digit Imaging."},{"key":"1482_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/3059170","volume":"2019","author":"M Klimont","year":"2019","unstructured":"Klimont M, Flieger M, Rzeszutek J, Stachera J, Zakrzewska A, Jo\u0144czyk-Potoczna K. Automated ventricular system segmentation in paediatric patients treated for hydrocephalus using deep learning methods. Biomed Res Int. 2019;2019:1-9. https:\/\/doi.org\/10.1155\/2019\/3059170.","journal-title":"Biomed Res Int."},{"key":"1482_CR34","doi-asserted-by":"publisher","unstructured":"Ono K, Iwamoto Y, Chen Y-W, Nonaka M. Automatic segmentation of infant brain ventricles with hydrocephalus in MRI based on 2.5D U-Net and transfer learning. JOIG. 2020;8(2):42\u201346. https:\/\/doi.org\/10.18178\/joig.8.2.42-46.","DOI":"10.18178\/joig.8.2.42-46"},{"key":"1482_CR35","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: Achievements and challenges. J Digit Imaging. 2019;32:582-596. https:\/\/doi.org\/10.1007\/s10278-019-00227-x.","journal-title":"J Digit Imaging."},{"key":"1482_CR36","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JAWM, Van Ginneken B, S\u00e1nchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. https:\/\/doi.org\/10.1016\/j.media.2017.07.005.","journal-title":"Med Image Anal."},{"key":"1482_CR37","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-248. https:\/\/doi.org\/10.1146\/annurev-bioeng-071516-044442.","journal-title":"Annu Rev Biomed Eng."},{"key":"1482_CR38","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-444. https:\/\/doi.org\/10.1038\/nature14539.","journal-title":"Nature."},{"key":"1482_CR39","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015;61:85-117. https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003.","journal-title":"Neural Networks."},{"key":"1482_CR40","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1001\/archneurpsyc.1942.02290060069004","volume":"47","author":"WA Evans","year":"1942","unstructured":"Evans WA. An encephalographic ratio for estimating ventricular enlargement and cerebral atrophy. Arch NeurPsych. 1942;47:931. https:\/\/doi.org\/10.1001\/archneurpsyc.1942.02290060069004.","journal-title":"Arch NeurPsych."},{"issue":"11","key":"1482_CR41","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1007\/s11548-019-02038-5","volume":"14","author":"TJ Huff","year":"2019","unstructured":"Huff TJ, Ludwig PE, Salazar D, Cramer JA. Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume. Int J Comput Assist Radiol Surg. 2019;14(11):1923-1932. https:\/\/doi.org\/10.1007\/s11548-019-02038-5.","journal-title":"Int J Comput Assist Radiol Surg."},{"issue":"1","key":"1482_CR42","doi-asserted-by":"publisher","first-page":"22166","DOI":"10.1038\/s41598-024-73167-4","volume":"14","author":"KT Huang","year":"2024","unstructured":"Huang KT, McNulty J, Hussein H, Klinger N, Chua MMJ, Ng PR, Chalif J, Mehta NH, Arnaout O. Automated ventricular segmentation and shunt failure detection using convolutional neural networks. Sci Rep. 2024;14(1):22166. https:\/\/doi.org\/10.1038\/s41598-024-73167-4.","journal-title":"Sci Rep."},{"issue":"10","key":"1482_CR43","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s00381-022-05552-1","volume":"38","author":"TR Ha","year":"2022","unstructured":"Ha TR, Quigley MF, Mozaffari K, Lathia O, Hofmann K, Myseros JS, Oluigbo C, Keating RF. Prediction of shunt failure facilitated by rapid and accurate volumetric analysis: a single institution\u2019s preliminary experience. Childs Nerv Syst. 2022;38(10):1907-1912. https:\/\/doi.org\/10.1007\/s00381-022-05552-1.","journal-title":"Childs Nerv Syst."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01482-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01482-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01482-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T19:25:13Z","timestamp":1765567513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01482-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1482"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01482-x","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,19]]},"assertion":[{"value":"30 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve activities that would require approval by a research ethics committee, as it utilized anonymized retrospective medical images which do not necessitate ethics committee approval.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}