{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T21:38:00Z","timestamp":1778708280521,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK129809"],"award-info":[{"award-number":["R01DK129809"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R56AG089080"],"award-info":[{"award-number":["R56AG089080"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-026-04864-2","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T08:25:48Z","timestamp":1774081548000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7609-3801","authenticated-orcid":false,"given":"Boyuan","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruting","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijiang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuya","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningshuo","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khashayar","family":"Vaziri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"4864_CR1","doi-asserted-by":"publisher","first-page":"13190","DOI":"10.1111\/obr.13190","volume":"22","author":"A Bosy-Westphal","year":"2021","unstructured":"Bosy-Westphal A, M\u00fcller MJ. Diagnosis of obesity based on body composition-associated health risks-time for a change in paradigm. Obes Rev. 2021;22:13190.","journal-title":"Obes Rev"},{"issue":"1","key":"4864_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s12893-024-02408-0","volume":"24","author":"R Suthakaran","year":"2024","unstructured":"Suthakaran R, Cao K, Arafat Y, Yeung J, Chan S, Master M, et al. Body composition assessment by artificial intelligence can be a predictive tool for short-term postoperative complications in hartmann\u2019s reversals. BMC Surg. 2024;24(1):111.","journal-title":"BMC Surg"},{"key":"4864_CR3","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1017\/gheg.2016.9","volume":"1","author":"J Wells","year":"2016","unstructured":"Wells J, Shirley M. Body composition and the monitoring of non-communicable chronic disease risk. Global health epidemiology and genomics. 2016;1:18.","journal-title":"Global health epidemiology and genomics"},{"key":"4864_CR4","doi-asserted-by":"crossref","unstructured":"Ngu YJ, Skalny AV, Tinkov AA, Tsai C-S, Chang C-C, Chuang Y-K, et al. Association between essential and non-essential metals, body composition, and metabolic syndrome in adults. Biol Trace Elem Res. 2022; 1\u201313.","DOI":"10.1007\/s12011-021-03077-3"},{"key":"4864_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.nut.2017.09.005","volume":"47","author":"N Tewari","year":"2018","unstructured":"Tewari N, Awad S, Macdonald IA, Lobo DN. A comparison of three methods to assess body composition. Nutrition. 2018;47:1\u20135.","journal-title":"Nutrition"},{"key":"4864_CR6","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.bone.2017.06.010","volume":"104","author":"JA Shepherd","year":"2017","unstructured":"Shepherd JA, Ng BK, Sommer MJ, Heymsfield SB. Body composition by dxa. Bone. 2017;104:101\u20135.","journal-title":"Bone"},{"key":"4864_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-5996-6-11","volume":"6","author":"Y Matsushita","year":"2014","unstructured":"Matsushita Y, Nakagawa T, Shinohara M, Yamamoto S, Takahashi Y, Mizoue T, et al. How can waist circumference predict the body composition? Diabetology & metabolic syndrome. 2014;6:1\u20137.","journal-title":"Diabetology & metabolic syndrome"},{"key":"4864_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.868627","volume":"13","author":"AW Potter","year":"2022","unstructured":"Potter AW, Tharion WJ, Holden LD, Pazmino A, Looney DP, Friedl KE. Circumference-based predictions of body fat revisited: Preliminary results from a us marine corps body composition survey. Front Physiol. 2022;13:868627.","journal-title":"Front Physiol"},{"issue":"3","key":"4864_CR9","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1108\/13612020310484852","volume":"7","author":"KP Simmons","year":"2003","unstructured":"Simmons KP, Istook CL. Body measurement techniques: Comparing 3d body-scanning and anthropometric methods for apparel applications. Journal of Fashion Marketing and Management An International Journal. 2003;7(3):306\u201332.","journal-title":"Journal of Fashion Marketing and Management An International Journal"},{"issue":"11","key":"4864_CR10","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1002\/oby.23256","volume":"29","author":"MC Wong","year":"2021","unstructured":"Wong MC, Ng BK, Tian I, Sobhiyeh S, Pagano I, Dechenaud M, et al. A pose-independent method for accurate and precise body composition from 3d optical scans. Obesity. 2021;29(11):1835\u201347.","journal-title":"Obesity"},{"issue":"11","key":"4864_CR11","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1002\/oby.22637","volume":"27","author":"MC Wong","year":"2019","unstructured":"Wong MC, Ng BK, Kennedy SF, Hwaung P, Liu EY, Kelly NN, et al. Children and adolescents\u2019 anthropometrics body composition from 3-d optical surface scans. Obesity. 2019;27(11):1738\u201349.","journal-title":"Obesity"},{"issue":"10","key":"4864_CR12","doi-asserted-by":"publisher","first-page":"6395","DOI":"10.1002\/mp.15843","volume":"49","author":"IY Tian","year":"2022","unstructured":"Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, et al. A device-agnostic shape model for automated body composition estimates from 3d optical scans. Med Phys. 2022;49(10):6395\u2013409.","journal-title":"Med Phys"},{"issue":"2","key":"4864_CR13","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1038\/s41430-018-0337-1","volume":"73","author":"JD Pleuss","year":"2019","unstructured":"Pleuss JD, Talty K, Morse S, Kuiper P, Scioletti M, Heymsfield SB, et al. A machine learning approach relating 3d body scans to body composition in humans. Eur J Clin Nutr. 2019;73(2):200\u20138.","journal-title":"Eur J Clin Nutr"},{"key":"4864_CR14","doi-asserted-by":"crossref","unstructured":"Feng B, Zheng,Y, Cheng R, Feng S, Vaziri K, Hahn J. Enhanced body composition estimation from 3d body scans. In: Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025), 2025;1:421\u2013431","DOI":"10.5220\/0013107000003911"},{"issue":"4","key":"4864_CR15","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.clnu.2011.12.011","volume":"31","author":"R Thibault","year":"2012","unstructured":"Thibault R, Genton L, Pichard C. Body composition: why, when and for who? Clin Nutr. 2012;31(4):435\u201347.","journal-title":"Clin Nutr"},{"issue":"8","key":"4864_CR16","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1016\/j.ejrad.2016.02.005","volume":"85","author":"A Andreoli","year":"2016","unstructured":"Andreoli A, Garaci F, Cafarelli FP, Guglielmi G. Body composition in clinical practice. Eur J Radiol. 2016;85(8):1461\u20138.","journal-title":"Eur J Radiol"},{"key":"4864_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109943","volume":"145","author":"A Tolonen","year":"2021","unstructured":"Tolonen A, Pakarinen T, Sassi A, Kytt\u00e4 J, Cancino W, Rinta-Kiikka I, et al. Methodology, clinical applications, and future directions of body composition analysis using computed tomography (ct) images: a review. Eur J Radiol. 2021;145:109943.","journal-title":"Eur J Radiol"},{"issue":"8","key":"4864_CR18","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1016\/j.ejrad.2016.04.004","volume":"85","author":"A Bazzocchi","year":"2016","unstructured":"Bazzocchi A, Ponti F, Albisinni U, Battista G, Guglielmi G. Dxa: Technical aspects and application. Eur J Radiol. 2016;85(8):1481\u201392.","journal-title":"Eur J Radiol"},{"issue":"1","key":"4864_CR19","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/oby.2011.211","volume":"20","author":"RJ Toombs","year":"2012","unstructured":"Toombs RJ, Ducher G, Shepherd JA, De Souza MJ. The impact of recent technological advances on the trueness and precision of dxa to assess body composition. Obesity. 2012;20(1):30\u20139.","journal-title":"Obesity"},{"issue":"2","key":"4864_CR20","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/s11547-009-0369-7","volume":"114","author":"A Andreoli","year":"2009","unstructured":"Andreoli A, Scalzo G, Masala S, Tarantino U, Guglielmi G. Body composition assessment by dual-energy x-ray absorptiometry (dxa). Radiol Med (Torino). 2009;114(2):286\u2013300.","journal-title":"Radiol Med (Torino)"},{"issue":"6","key":"4864_CR21","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1002\/mrm.28360","volume":"84","author":"M Borga","year":"2020","unstructured":"Borga M, Ahlgren A, Romu T, Widholm P, Dahlqvist Leinhard O, West J. Reproducibility and repeatability of mri-based body composition analysis. Magn Reson Med. 2020;84(6):3146\u201356.","journal-title":"Magn Reson Med"},{"key":"4864_CR22","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1007\/s40520-016-0589-3","volume":"28","author":"G Guglielmi","year":"2016","unstructured":"Guglielmi G, Ponti F, Agostini M, Amadori M, Battista G, Bazzocchi A. The role of dxa in sarcopenia. Aging Clin Exp Res. 2016;28:1047\u201360.","journal-title":"Aging Clin Exp Res"},{"issue":"2","key":"4864_CR23","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3390\/mi14020464","volume":"14","author":"Z Sun","year":"2023","unstructured":"Sun Z, Wong YH, Yeong CH. Patient-specific 3d-printed low-cost models in medical education and clinical practice. Micromachines. 2023;14(2):464.","journal-title":"Micromachines"},{"issue":"2","key":"4864_CR24","doi-asserted-by":"publisher","first-page":"534","DOI":"10.3892\/etm.2022.11461","volume":"24","author":"H-M Shi","year":"2022","unstructured":"Shi H-M, Sun Z-C, Ju F-H. Understanding the harm of low-dose computed tomography radiation to the body. Exp Ther Med. 2022;24(2):534.","journal-title":"Exp Ther Med"},{"issue":"8","key":"4864_CR25","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.3390\/nu13082493","volume":"13","author":"CJ Holmes","year":"2021","unstructured":"Holmes CJ, Racette SB. The utility of body composition assessment in nutrition and clinical practice: an overview of current methodology. Nutrients. 2021;13(8):2493.","journal-title":"Nutrients"},{"key":"4864_CR26","doi-asserted-by":"publisher","first-page":"6384","DOI":"10.1007\/s00330-021-07709-z","volume":"31","author":"H Arabi","year":"2021","unstructured":"Arabi H, Zaidi H. Deep learning-based metal artefact reduction in pet\/ct imaging. Eur Radiol. 2021;31:6384\u201396.","journal-title":"Eur Radiol"},{"issue":"22","key":"4864_CR27","doi-asserted-by":"publisher","first-page":"4792","DOI":"10.3390\/nu15224792","volume":"15","author":"MG Branco","year":"2023","unstructured":"Branco MG, Mateus C, Capelas ML, Pimenta N, Santos T, M\u00e4kitie A, et al. Bioelectrical impedance analysis (bia) for the assessment of body composition in oncology: a scoping review. Nutrients. 2023;15(22):4792.","journal-title":"Nutrients"},{"issue":"7","key":"4864_CR28","doi-asserted-by":"publisher","first-page":"0200465","DOI":"10.1371\/journal.pone.0200465","volume":"13","author":"N Achamrah","year":"2018","unstructured":"Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, et al. Comparison of body composition assessment by dxa and bia according to the body mass index: A retrospective study on 3655 measures. PLoS ONE. 2018;13(7):0200465.","journal-title":"PLoS ONE"},{"issue":"7","key":"4864_CR29","doi-asserted-by":"publisher","first-page":"3480","DOI":"10.3390\/app15073480","volume":"15","author":"R Smolik","year":"2025","unstructured":"Smolik R, Gawe\u0142 M, Kliszczyk D, Sasin N, Szewczyk K, G\u00f3rnicka M. Comparative analysis of body composition results obtained by air displacement plethysmography (adp) and bioelectrical impedance analysis (bia) in adults. Appl Sci. 2025;15(7):3480.","journal-title":"Appl Sci"},{"key":"4864_CR30","doi-asserted-by":"crossref","unstructured":"Miller J. Body composition. In: Laboratory Manual for Strength and Conditioning, pp. 61\u201389. Routledge, ??? 2023","DOI":"10.4324\/9781003186762-5"},{"issue":"4","key":"4864_CR31","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.glohj.2022.11.001","volume":"6","author":"M Javaid","year":"2022","unstructured":"Javaid M, Haleem A, Singh RP, Suman R. 3d printing applications for healthcare research and development. Global Health Journal. 2022;6(4):217\u201326.","journal-title":"Global Health Journal"},{"issue":"7","key":"4864_CR32","doi-asserted-by":"publisher","first-page":"58","DOI":"10.4156\/jdcta.vol4.issue7.6","volume":"4","author":"PR Apeagyei","year":"2010","unstructured":"Apeagyei PR, et al. Application of 3d body scanning technology to human measurement for clothing fit. International Journal of Digital Content Technology and its Applications. 2010;4(7):58\u201368.","journal-title":"International Journal of Digital Content Technology and its Applications"},{"key":"4864_CR33","doi-asserted-by":"publisher","first-page":"67281","DOI":"10.1109\/ACCESS.2021.3076595","volume":"9","author":"K Bartol","year":"2021","unstructured":"Bartol K, Bojani\u0107 D, Petkovi\u0107 T, Pribani\u0107 T. A review of body measurement using 3d scanning. Ieee Access. 2021;9:67281\u2013301.","journal-title":"Ieee Access"},{"key":"4864_CR34","doi-asserted-by":"crossref","unstructured":"Daanen, H.A., Psikuta, A.: 3d body scanning. In: Automation in Garment Manufacturing, pp. 237\u2013252. Elsevier, ??? (2018)","DOI":"10.1016\/B978-0-08-101211-6.00010-0"},{"issue":"12","key":"4864_CR35","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1002\/mp.14492","volume":"47","author":"IY Tian","year":"2020","unstructured":"Tian IY, Ng BK, Wong MC, Kennedy S, Hwaung P, Kelly N, et al. Predicting 3d body shape and body composition from conventional 2d photography. Med Phys. 2020;47(12):6232\u201345.","journal-title":"Med Phys"},{"issue":"4","key":"4864_CR36","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/s40846-023-00816-w","volume":"43","author":"X Ma","year":"2023","unstructured":"Ma X, Wang D, Li W, Dai X, Liu S, Li Z. Estimation of the skeletal muscle cross-sectional area of the lower extremity using structured light three-dimensional scanning technology. Journal of Medical and Biological Engineering. 2023;43(4):446\u201353.","journal-title":"Journal of Medical and Biological Engineering"},{"issue":"11","key":"4864_CR37","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1038\/ejcn.2016.109","volume":"70","author":"BK Ng","year":"2016","unstructured":"Ng BK, Hinton BJ, Fan B, Kanaya AM, Shepherd JA. Clinical anthropometrics and body composition from 3d whole-body surface scans. Eur J Clin Nutr. 2016;70(11):1265\u201370.","journal-title":"Eur J Clin Nutr"},{"issue":"6","key":"4864_CR38","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1002\/oby.23434","volume":"30","author":"B Smith","year":"2022","unstructured":"Smith B, McCarthy C, Dechenaud ME, Wong MC, Shepherd J, Heymsfield SB. Anthropometric evaluation of a 3d scanning mobile application. Obesity. 2022;30(6):1181\u20138.","journal-title":"Obesity"},{"key":"4864_CR39","doi-asserted-by":"crossref","unstructured":"Munir V, Dempster E, Lyons D. Innovative strategies for 3d visualisation using photogrammetry and 3d scanning for mobile phones. In: EVA London Conference, pp. 231\u2013236 (2019). BCS, The Chartered Institute for IT","DOI":"10.14236\/ewic\/EVA2019.43"},{"issue":"4","key":"4864_CR40","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1504\/IJDH.2016.084581","volume":"1","author":"A Ballester","year":"2016","unstructured":"Ballester A, Parrilla E, Pi\u00e9rola A, Uriel J, P\u00e9rez C, Piqueras P, et al. Data-driven three-dimensional reconstruction of human bodies using a mobile phone app. International Journal of the Digital Human. 2016;1(4):361\u201388.","journal-title":"International Journal of the Digital Human"},{"key":"4864_CR41","doi-asserted-by":"publisher","first-page":"27939","DOI":"10.1109\/ACCESS.2018.2837147","volume":"6","author":"D Song","year":"2018","unstructured":"Song D, Tong R, Du J, Zhang Y, Jin Y. Data-driven 3-d human body customization with a mobile device. IEEE Access. 2018;6:27939\u201348.","journal-title":"IEEE Access"},{"key":"4864_CR42","doi-asserted-by":"publisher","unstructured":"Li W, Xiao X, Hahn J. 3d reconstruction and texture optimization using a sparse set of rgb-d cameras. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1413\u20131422 (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00155","DOI":"10.1109\/WACV.2019.00155"},{"issue":"1","key":"4864_CR43","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1038\/s41746-024-01289-0","volume":"7","author":"C Qiao","year":"2024","unstructured":"Qiao C, Rolfe EDL, Mak E, Sengupta A, Powell R, Watson LP, et al. Prediction of total and regional body composition from 3d body shape. NPJ Digital Medicine. 2024;7(1):298.","journal-title":"NPJ Digital Medicine"},{"issue":"6","key":"4864_CR44","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1093\/ajcn\/nqz218","volume":"110","author":"BK Ng","year":"2019","unstructured":"Ng BK, Sommer MJ, Wong MC, Pagano I, Nie Y, Fan B, et al. Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the shape up! studies. Am J Clin Nutr. 2019;110(6):1316\u201326.","journal-title":"Am J Clin Nutr"},{"issue":"10","key":"4864_CR45","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1038\/s41430-023-01309-4","volume":"77","author":"M Guarnieri Lopez","year":"2023","unstructured":"Guarnieri Lopez M, Matthes KL, Sob C, Bender N, Staub K. Associations between 3d surface scanner derived anthropometric measurements and body composition in a cross-sectional study. Eur J Clin Nutr. 2023;77(10):972\u201381.","journal-title":"Eur J Clin Nutr"},{"issue":"1","key":"4864_CR46","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1038\/s41746-025-01469-6","volume":"8","author":"IY Tian","year":"2025","unstructured":"Tian IY, Liu J, Wong MC, Kelly NN, Liu YE, Garber AK, et al. 3d convolutional deep learning for nonlinear estimation of body composition from whole body morphology. npj Digital Medicine. 2025;8(1):79.","journal-title":"npj Digital Medicine"},{"issue":"2","key":"4864_CR47","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1017\/S0029665115004206","volume":"75","author":"MJ M\u00fcller","year":"2016","unstructured":"M\u00fcller MJ, Braun W, Pourhassan M, Geisler C, Bosy-Westphal A. Application of standards and models in body composition analysis. Proceedings of the Nutrition Society. 2016;75(2):181\u20137.","journal-title":"Proceedings of the Nutrition Society"},{"key":"4864_CR48","doi-asserted-by":"crossref","unstructured":"Mo K, Zhu S, Chang AX, Yi L, Tripathi S, Guibas LJ, Su H. Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 909\u2013918 (2019)","DOI":"10.1109\/CVPR.2019.00100"},{"issue":"1","key":"4864_CR49","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.1038\/s41598-023-30434-0","volume":"13","author":"S Jeon","year":"2023","unstructured":"Jeon S, Kim M, Yoon J, Lee S, Youm S. Machine learning-based obesity classification considering 3d body scanner measurements. Sci Rep. 2023;13(1):3299.","journal-title":"Sci Rep"},{"issue":"3","key":"4864_CR50","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1148\/radiol.2018181432","volume":"290","author":"AD Weston","year":"2019","unstructured":"Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated abdominal segmentation of ct scans for body composition analysis using deep learning. Radiology. 2019;290(3):669\u201379.","journal-title":"Radiology"},{"issue":"5","key":"4864_CR51","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1093\/advances\/nmab016","volume":"12","author":"MN Blue","year":"2021","unstructured":"Blue MN, Tinsley GM, Ryan ED, Smith-Ryan AE. Validity of body-composition methods across racial and ethnic populations. Adv Nutr. 2021;12(5):1854\u201362.","journal-title":"Adv Nutr"},{"issue":"5","key":"4864_CR52","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1007\/s00192-022-05317-z","volume":"34","author":"J Chen","year":"2023","unstructured":"Chen J, Peng L, Xiang L, Li B, Shen H, Luo D. Association between body mass index, trunk and total body fat percentage with urinary incontinence in adult us population. Int Urogynecol J. 2023;34(5):1075\u201382.","journal-title":"Int Urogynecol J"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04864-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-04864-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04864-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T10:01:59Z","timestamp":1774087319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-04864-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["4864"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-04864-2","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]},"assertion":[{"value":"29 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"292"}}