{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:56:27Z","timestamp":1770962187699,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE)","award":["2021RIS-001"],"award-info":[{"award-number":["2021RIS-001"]}]},{"name":"Ministry of Education (MOE)","award":["20020499"],"award-info":[{"award-number":["20020499"]}]},{"name":"Ministry of Education (MOE)","award":["20022814"],"award-info":[{"award-number":["20022814"]}]},{"name":"Technology Innovation Program","award":["2021RIS-001"],"award-info":[{"award-number":["2021RIS-001"]}]},{"name":"Technology Innovation Program","award":["20020499"],"award-info":[{"award-number":["20020499"]}]},{"name":"Technology Innovation Program","award":["20022814"],"award-info":[{"award-number":["20022814"]}]},{"name":"Ministry of Trade, Industry, and Energy (MOTIE), Korea","award":["2021RIS-001"],"award-info":[{"award-number":["2021RIS-001"]}]},{"name":"Ministry of Trade, Industry, and Energy (MOTIE), Korea","award":["20020499"],"award-info":[{"award-number":["20020499"]}]},{"name":"Ministry of Trade, Industry, and Energy (MOTIE), Korea","award":["20022814"],"award-info":[{"award-number":["20022814"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5\u00b0. Further bolstering the study\u2019s credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.<\/jats:p>","DOI":"10.3390\/bioengineering10101236","type":"journal-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T10:32:24Z","timestamp":1698057144000},"page":"1236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points"],"prefix":"10.3390","volume":"10","author":[{"given":"Thong Phi","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea"},{"name":"Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Hwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seong-Ha","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea"},{"name":"Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea"},{"name":"AIDICOME Inc., 221, 5th Engineering Building, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Hoon","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1007\/s00586-011-1939-7","article-title":"Equilibrium of the human body and the gravity line: The basics","volume":"20","author":"Saddiki","year":"2011","journal-title":"Eur. Spine J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"958","DOI":"10.31616\/asj.2021.0451","article-title":"Reciprocal changes in the entire body following realignment surgery for adult spinal deformities","volume":"16","author":"Lee","year":"2022","journal-title":"Asian Spine J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1097\/BRS.0000000000000844","article-title":"Recruitment of compensatory mechanisms in sagittal spinal malalignment is age-and regional deformity-dependent: A full-standing axis analysis of key radiographic parameters","volume":"40","author":"Diebo","year":"2015","journal-title":"Spine"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"918","DOI":"10.31616\/asj.2021.0399","article-title":"Relationship between lower-limb pain intensity and dynamic lumbopelvic hip alignment in patients with degenerative lumbar spinal canal stenosis: A cross-sectional study","volume":"16","author":"Miura","year":"2022","journal-title":"Asian Spine J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"E5","DOI":"10.3171\/2017.8.FOCUS17498","article-title":"Relative lumbar lordosis and lordosis distribution index: Individualized pelvic incidence-based proportional parameters that quantify lumbar lordosis more precisely than pelvic incidence minus lumbar lordosis","volume":"43","author":"Yilgor","year":"2017","journal-title":"Neurosurg. Focus"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3171\/2018.5.SPINE171232","article-title":"Cervical mismatch: Normative value of t1 slope minus cervical lordosis and its ability to predict ideal cervical lordosis","volume":"30","author":"Staub","year":"2018","journal-title":"J. Neurosurg. Spine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e1312","DOI":"10.1002\/widm.1312","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1001\/jamainternmed.2018.7117","article-title":"Deep Learning in Medicine-Promise, Progress, and Challenges","volume":"179","author":"Wang","year":"2019","journal-title":"JAMA Intern. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2796","DOI":"10.1109\/TMI.2019.2914400","article-title":"Toward automated 3D spine reconstruction from biplanar radiographs using a CNN for statistical spine model fitting","volume":"38","author":"Aubert","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Weng, C.H., Wang, C.L., Huang, Y.J., Yeh, Y.C., Fu, C.J., Yeh, C.Y., and Tsai, T.-T. (2019). Artificial intelligence for automatic measurement of sagittal vertical axis using the ResUNet framework. J. Clin. Med., 8.","DOI":"10.3390\/jcm8111826"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1177\/2192568219868190","article-title":"Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision","volume":"10","author":"Cho","year":"2020","journal-title":"Glob. Spine J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.05.005","article-title":"Automated comprehensive Adolescent Idiopathic Scoliosis assessment using the MVC-net","volume":"48","author":"Wu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chae, D.S., Nguyen, T.P., Park, S.J., Kang, K.Y., Won, C., and Yoon, J. (2020). Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters. Biomed. Comput. Methods Programs Biomed., 197.","DOI":"10.1016\/j.cmpb.2020.105699"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nguyen, T.P., Chae, D.S., Park, S.J., Kang, K., and Yoon, J. (2021). Deep learning system for modeling classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis. Biomed. Signal Process. Control, 65.","DOI":"10.1016\/j.bspc.2020.102371"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nguyen, T.P., Chae, D.S., Park, S.J., Kang, K.Y., Lee, W.S., and Yoon, J. (2020). Intelligent analysis of coronal alignment in lower limbs based on radiographic image with convolutional neural networ. Comput. Biol. Med., 120.","DOI":"10.1016\/j.compbiomed.2020.103732"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s10278-021-00533-3","article-title":"Intelligent Evaluation of Global Spinal Alignment Using a Decentralized CNN","volume":"35","author":"Nguyen","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00586-020-06329-3","article-title":"Influence of spinopelvic morphology on sagittal spinal alignment: Analysis of the incidence angle of inflection points","volume":"29","author":"Choi","year":"2020","journal-title":"Eur. Spine J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e826","DOI":"10.1016\/j.wneu.2019.03.274","article-title":"Effectiveness of the c2 incidence angle in evaluating global spinopelvic alignment in patients with mild degenerative spondylosis","volume":"127","author":"Choi","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"E671","DOI":"10.1097\/BRS.0000000000003830","article-title":"Deep learning automates measurement of spinopelvic parameters on lateral lumbar radiographs","volume":"46","author":"Schwartz","year":"2021","journal-title":"Spine"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9482","DOI":"10.1038\/s41598-021-89102-w","article-title":"2-step deep learning model for landmarks localization in spine radiographs","volume":"11","author":"Cina","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_21","first-page":"2865","article-title":"[Abstracts of the 15th German Spine Congress] Can artificial intelligence support or even replace physicians in measuring the sagittal balance?\u2014A validation study on preoperative and postoperative images of 170 patients","volume":"29","author":"Grover","year":"2020","journal-title":"Eur. Spine J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tran, V.L., Lin, H.-Y., and Liu, H.-W. (December, January 30). MBNet: A multi-task deep neural network for semantic segmentation and lumbar vertebra inspection on X-ray images. Proceedings of the 15th Asian Conference on Computer Vision\u2014ACCV 2020, Kyoto, Japan.","DOI":"10.1007\/978-3-030-69541-5_38"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100062","DOI":"10.1016\/j.bas.2021.100062","article-title":"[Abstracts of EUROSPINE 2021] Can artificial intelligence support or even replace physicians in measuring the sagittal balance? A validation study on preoperative and postoperative images of 170 patients","volume":"1","author":"Franke","year":"2021","journal-title":"Brain Spine"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110071","DOI":"10.1016\/j.ejrad.2021.110071","article-title":"Artificial intelligence X-ray measurement technology of anatomical parameters related to lumbosacral stability","volume":"146","author":"Zhou","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: A self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/JSEN.2019.2956072","article-title":"Time\u2013frequency domain deep convolutional neural network for the classification of focal and nonfocal EEG signals","volume":"20","author":"Madhavan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_27","unstructured":"Arena, P., Fortuna, L., Occhipinti, L., and Xibilia, M.G. (June, January 30). Neural networks for quaternion-valued function approximation. Proceedings of the IEEE International Symposium on Circuits and Systems\u2014ISCAS\u201994, London, UK."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/JBHI.2016.2635662","article-title":"Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domains","volume":"21","author":"Zhang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1007\/s00586-019-05944-z","article-title":"Fully automated radiological analysis of spinal disorders and deformities: A deep-learning approach","volume":"28","author":"Galbusera","year":"2019","journal-title":"Eur. Spine J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7618","DOI":"10.1038\/s41598-021-87141-x","article-title":"Deep-learning approach for automatic landmark detection and alignment analysis of whole-spine lateral radiographs","volume":"11","author":"Yeh","year":"2021","journal-title":"Sci. Rep."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/10\/1236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:10:19Z","timestamp":1760130619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/10\/1236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,23]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["bioengineering10101236"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering10101236","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,23]]}}}