{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:12:25Z","timestamp":1770819145749,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"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"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s10278-022-00671-2","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:31:15Z","timestamp":1657153875000},"page":"1494-1505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs"],"prefix":"10.1007","volume":"35","author":[{"given":"Nathan","family":"Larson","sequence":"first","affiliation":[]},{"given":"Chantal","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Bao","family":"Do","sequence":"additional","affiliation":[]},{"given":"Aryan","family":"Kaul","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Larson","sequence":"additional","affiliation":[]},{"given":"Shannon","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Erin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Bultman","sequence":"additional","affiliation":[]},{"given":"Kate","family":"Stevens","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Pai","sequence":"additional","affiliation":[]},{"given":"Audrey","family":"Ha","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Boutin","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Fredericson","sequence":"additional","affiliation":[]},{"given":"Long","family":"Do","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5612-8511","authenticated-orcid":false,"given":"Charles","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"671_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.gaitpost.2017.05.028","volume":"57","author":"S Khamis","year":"2017","unstructured":"Khamis, S. and E. Carmeli, Relationship and significance of gait deviations associated with limb length discrepancy: a systematic review. Gait & posture, 2017. 57: p. 115-123.","journal-title":"Gait & posture"},{"issue":"3","key":"671_CR2","doi-asserted-by":"publisher","first-page":"393","DOI":"10.5114\/aoms.2010.14262","volume":"6","author":"JW Raczkowski","year":"2010","unstructured":"Raczkowski, J.W., B. Daniszewska, and K. Zolynski, Functional scoliosis caused by leg length discrepancy. Archives of medical science: AMS, 2010. 6(3): p. 393.","journal-title":"Archives of medical science: AMS"},{"issue":"1","key":"671_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2474-13-95","volume":"13","author":"C R\u00f6der","year":"2012","unstructured":"R\u00f6der, C., et al., Total hip arthroplasty: leg length inequality impairs functional outcomes and patient satisfaction. BMC musculoskeletal disorders, 2012. 13(1): p. 1-8.","journal-title":"BMC musculoskeletal disorders"},{"issue":"3","key":"671_CR4","doi-asserted-by":"publisher","first-page":"300","DOI":"10.4103\/0019-5413.65159","volume":"44","author":"SV Vaidya","year":"2010","unstructured":"Vaidya, S.V., et al., Total knee arthroplasty: limb length discrepancy and functional outcome. Indian journal of orthopaedics, 2010. 44(3): p. 300-307.","journal-title":"Indian journal of orthopaedics"},{"issue":"2","key":"671_CR5","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.clinbiomech.2005.09.001","volume":"21","author":"D Moschella","year":"2006","unstructured":"Moschella, D., et al., Wear patterns on tibial plateau from varus osteoarthritic knees. Clinical Biomechanics, 2006. 21(2): p. 152-158.","journal-title":"Clinical Biomechanics"},{"issue":"5","key":"671_CR6","doi-asserted-by":"publisher","first-page":"452","DOI":"10.5301\/hipint.5000276","volume":"25","author":"A Sykes","year":"2015","unstructured":"Sykes, A., et al., Patients' perception of leg length discrepancy post total hip arthroplasty. Hip Int, 2015. 25(5): p. 452-6.","journal-title":"Hip Int"},{"issue":"4","key":"671_CR7","doi-asserted-by":"publisher","first-page":"326","DOI":"10.5792\/ksrr.18.028","volume":"30","author":"SH Kim","year":"2018","unstructured":"Kim, S.H., et al., Reliability and validity of the femorotibial mechanical axis angle in primary total knee arthroplasty: navigation versus weight bearing or supine whole leg radiographs. Knee surgery & related research, 2018. 30(4): p. 326.","journal-title":"Knee surgery & related research"},{"key":"671_CR8","doi-asserted-by":"crossref","unstructured":"Schock, J., et al., Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence. Radiology: Artificial Intelligence, 2020. 3(2): p. e200198.","DOI":"10.1148\/ryai.2020200198"},{"issue":"3","key":"671_CR9","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.knee.2020.01.015","volume":"27","author":"MYT Hau","year":"2020","unstructured":"Hau, M.Y.T., et al., Two-dimensional\/three-dimensional EOS\u2122 imaging is reliable and comparable to traditional X-ray imaging assessment of knee osteoarthritis aiding surgical management. The Knee, 2020. 27(3): p. 970-979.","journal-title":"The Knee"},{"issue":"12","key":"671_CR10","doi-asserted-by":"publisher","first-page":"2910","DOI":"10.1007\/s11999-008-0524-9","volume":"466","author":"S Sabharwal","year":"2008","unstructured":"Sabharwal, S. and A. Kumar, Methods for assessing leg length discrepancy. Clinical orthopaedics and related research, 2008. 466(12): p. 2910-2922.","journal-title":"Clinical orthopaedics and related research"},{"key":"671_CR11","doi-asserted-by":"crossref","unstructured":"Moreland, J.R., L. Bassett, and G. Hanker, Radiographic analysis of the axial alignment of the lower extremity. The Journal of bone and joint surgery. American volume, 1987. 69(5): p. 745\u2013749.","DOI":"10.2106\/00004623-198769050-00016"},{"issue":"6","key":"671_CR12","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s00586-014-3739-3","volume":"24","author":"M Tyrakowski","year":"2015","unstructured":"Tyrakowski, M., H. Yu, and K. Siemionow, Pelvic incidence and pelvic tilt measurements using femoral heads or acetabular domes to identify centers of the hips: comparison of two methods. European Spine Journal, 2015. 24(6): p. 1259-1264.","journal-title":"European Spine Journal"},{"issue":"3","key":"671_CR13","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s00256-005-0050-8","volume":"35","author":"M Tannast","year":"2006","unstructured":"Tannast, M., et al., Estimation of pelvic tilt on anteroposterior X-rays\u2014a comparison of six parameters. Skeletal radiology, 2006. 35(3): p. 149-155.","journal-title":"Skeletal radiology"},{"key":"671_CR14","unstructured":"Dutta, A., A. Gupta, and A. Zissermann, VGG image annotator (VIA). URL: http:\/\/www.robots. ox. ac. uk\/~ vgg\/software\/via, 2016."},{"key":"671_CR15","unstructured":"Faster-RCNN ResNet 101 Coco Config. 2018, GitHub."},{"issue":"3","key":"671_CR16","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s11548-019-02096-9","volume":"15","author":"B Liu","year":"2020","unstructured":"Liu, B., J. Luo, and H. Huang, Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN. International journal of computer assisted radiology and surgery, 2020. 15(3): p. 457-466.","journal-title":"International journal of computer assisted radiology and surgery"},{"key":"671_CR17","unstructured":"Tan, M. and Q.V. Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv e-prints, 2019: p. arXiv:1905.11946."},{"key":"671_CR18","doi-asserted-by":"crossref","unstructured":"Marques, G., D. Agarwal, and I. de la Torre D\u00edez, Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Applied soft computing, 2020. 96: p. 106691.","DOI":"10.1016\/j.asoc.2020.106691"},{"key":"671_CR19","doi-asserted-by":"crossref","unstructured":"Chetoui, M. and M.A. Akhloufi. Explainable Diabetic Retinopathy using EfficientNET. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020. IEEE.","DOI":"10.1109\/EMBC44109.2020.9175664"},{"issue":"11","key":"671_CR20","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.3390\/biom10111534","volume":"10","author":"N Yamamoto","year":"2020","unstructured":"Yamamoto, N., et al., Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates. Biomolecules, 2020. 10(11): p. 1534.","journal-title":"Biomolecules"},{"key":"671_CR21","doi-asserted-by":"crossref","unstructured":"Xie, Q., et al., Self-training with Noisy Student improves ImageNet classification. arXiv e-prints, art. arXiv preprint arXiv:1911.04252, 2019.","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"671_CR22","unstructured":"Wightman, R., PyTorch Image Models. 2019, GitHub."},{"key":"671_CR23","unstructured":"Howard, J. and R. Thomas, fast. ai-Making neural networks uncool again."},{"issue":"2","key":"671_CR24","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo, T.K. and M.Y. Li, A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 2016. 15(2): p. 155-163.","journal-title":"Journal of Chiropractic Medicine"},{"issue":"1","key":"671_CR25","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1148\/radiol.2020192003","volume":"296","author":"Q Zheng","year":"2020","unstructured":"Zheng, Q., et al., Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs. Radiology, 2020. 296(1): p. 152-158.","journal-title":"Radiology"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00671-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00671-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00671-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T20:25:37Z","timestamp":1744143937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00671-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":25,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["671"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00671-2","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"15 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","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 research study (IRB-57973) was conducted retrospectively from data obtained for clinical purposes. An IRB official waiver of ethical approval was granted by the Stanford University IRB.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Waived, as only images were obtained for this HIPAA compliant retrospective review.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Waived, as only images were obtained for this HIPAA compliant retrospective review.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare that they have no conflicts of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests\/Competing Interests"}}]}}