{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T19:32:35Z","timestamp":1786476755816,"version":"build-2736575974"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Spine Deform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Adolescent Idiopathic Scoliosis (AIS) affects 2\u20134% of adolescents globally. Current screening methods face challenges, including high false-positive rates, underscoring the need for safer, more accurate alternatives to improve early detection and reduce unnecessary imaging. This scoping review evaluates Artificial Intelligence (AI) applications in non-radiological AIS screening and diagnosis, aiming to (1) identify current AI models, (2) assess performance metrics, (3) examine validation methods, and (4) evaluate feasibility for large-scale implementation in current healthcare settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Following PRISMA-ScR guidelines, 16 studies (2001\u20132025) were analyzed from PubMed, Scopus, and Web of Science. Inclusion criteria encompassed studies using AI with non-radiological inputs (RGB images, 3D scans, depth maps). Data on model types, performance metrics, validation, and clinical implications were synthesized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Convolutional Neural Networks (CNNs; 71.4%) predominated, followed by Support Vector Machines (21.4%) and Artificial Neural Networks (7.14%). AI models achieved a weighted mean accuracy of 91.1% (range: 72.4\u201397.5%) for curve classification and Cobb angle prediction errors of 5.5\u00b0\u2009\u00b1\u20093.5\u00b0. For diagnosing scoliosis (curves\u2009&gt;\u200910\u00b0), models achieved\u2009\u2265\u200987.5% sensitivity (up to 97.4% with advanced models) and specificity\u2009\u2265\u200983.5% (up to 99% with high-precision algorithms).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and conclusions<\/jats:title>\n                    <jats:p>AI-based non-radiological AIS screening demonstrates high diagnostic accuracy and offers a promising alternative to traditional methods by providing accessible, multi-output assessments using smartphone cameras or 3D scanners. Key limitations include small sample sizes, inconsistent validation, and ethical concerns (e.g., data privacy and automation bias). Future research should prioritize randomized controlled trials, standardized protocols, and multi-level stakeholder engagement to facilitate clinical integration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s43390-026-01517-5","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T19:15:10Z","timestamp":1786475710000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A scoping review on the application of new technology in the screening and diagnosis of adolescent idiopathic scoliosis"],"prefix":"10.1007","author":[{"given":"Nandini","family":"Dulani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamie Tsun","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Wai Ling","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth Guangpu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheuk Kin","family":"Kwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0321-8350","authenticated-orcid":false,"given":"Adam Yiu Chung","family":"Lau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"issue":"1","key":"1517_CR1","doi-asserted-by":"publisher","first-page":"15030","DOI":"10.1038\/nrdp.2015.30","volume":"1","author":"JC Cheng","year":"2015","unstructured":"Cheng JC, Castelein RM, Chu WC, Danielsson AJ, Dobbs MB, Grivas TB et al (2015) Adolescent idiopathic scoliosis. Nat Rev Dis Primers 1(1):15030. https:\/\/doi.org\/10.1038\/nrdp.2015.30","journal-title":"Nat Rev Dis Primers"},{"issue":"6","key":"1517_CR2","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1097\/bpo.0000000000001808","volume":"41","author":"JJ Thomas","year":"2021","unstructured":"Thomas JJ, Stans AA, Milbrandt TA, Kremers HM, Shaughnessy WJ, Larson AN (2021) Trends in incidence of adolescent idiopathic scoliosis: a modern US population-based study. J Pediatr Orthop 41(6):327\u2013332. https:\/\/doi.org\/10.1097\/bpo.0000000000001808","journal-title":"J Pediatr Orthop"},{"issue":"6","key":"1517_CR3","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s00381-020-04608-4","volume":"36","author":"D Addai","year":"2020","unstructured":"Addai D, Zarkos J, Bowey AJ (2020) Current concepts in the diagnosis and management of adolescent idiopathic scoliosis. Childs Nerv Syst 36(6):1111\u20131119. https:\/\/doi.org\/10.1007\/s00381-020-04608-4","journal-title":"Childs Nerv Syst"},{"issue":"10","key":"1517_CR4","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1097\/brs.0b013e3181bcc835","volume":"35","author":"DYT Fong","year":"2010","unstructured":"Fong DYT, Lee CF, Cheung KMC, Cheng JCY, Ng BKW, Lam TP et al (2010) A meta-analysis of the clinical effectiveness of school scoliosis screening. Spine 35(10):1061\u20131071. https:\/\/doi.org\/10.1097\/brs.0b013e3181bcc835","journal-title":"Spine"},{"issue":"3","key":"1517_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.4184\/asj.2015.9.3.407","volume":"9","author":"S Yamamoto","year":"2015","unstructured":"Yamamoto S, Shigematsu H, Kadono F, Tanaka Y, Tatematsu M, Okuda A et al (2015) Adolescent scoliosis screening in Nara City schools: a 23-year retrospective cross-sectional study. Asian Spine J 9(3):407. https:\/\/doi.org\/10.4184\/asj.2015.9.3.407","journal-title":"Asian Spine J"},{"issue":"10","key":"1517_CR6","doi-asserted-by":"publisher","first-page":"3366","DOI":"10.1007\/s00586-016-4747-2","volume":"25","author":"A Simony","year":"2016","unstructured":"Simony A, Hansen EJ, Christensen SB, Carreon LY, Andersen MO (2016) Incidence of cancer in adolescent idiopathic scoliosis patients treated 25 years previously. Eur Spine J 25(10):3366\u20133370. https:\/\/doi.org\/10.1007\/s00586-016-4747-2","journal-title":"Eur Spine J"},{"issue":"1","key":"1517_CR7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s13013-016-0106-7","volume":"11","author":"SCN Hui","year":"2016","unstructured":"Hui SCN, Pialasse J-P, Wong JYH, Lam T-p, Ng BKW, Cheng JCY et al (2016) Radiation dose of digital radiography (DR) versus micro-dose x-ray (EOS) on patients with adolescent idiopathic scoliosis: 2016 SOSORT- IRSSD \u201cJohn Sevastic Award\u201d winner in imaging research. Scoliosis Spinal Disord 11(1):46. https:\/\/doi.org\/10.1186\/s13013-016-0106-7","journal-title":"Scoliosis Spinal Disord"},{"issue":"14","key":"1517_CR8","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.3390\/diagnostics13142431","volume":"13","author":"A Tabard-Foug\u00e8re","year":"2023","unstructured":"Tabard-Foug\u00e8re A, de Bodman C, Dhouib A, Bonnefoy-Mazure A, Armand S, Dayer R (2023) Three-dimensional spinal evaluation using rasterstereography in patients with adolescent idiopathic scoliosis: is it closer to three-dimensional or two-dimensional radiography? Diagnostics (Basel) 13(14):2431. https:\/\/doi.org\/10.3390\/diagnostics13142431","journal-title":"Diagnostics (Basel)"},{"issue":"2","key":"1517_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1097\/brs.0000000000001679","volume":"42","author":"A Tabard-Foug\u00e8re","year":"2017","unstructured":"Tabard-Foug\u00e8re A, Bonnefoy-Mazure A, Hanquinet S, Lascombes P, Armand S, Dayer R (2017) Validity and reliability of spine rasterstereography in patients with adolescent idiopathic scoliosis. Spine (Phila Pa 1976) 42(2):98\u2013105. https:\/\/doi.org\/10.1097\/brs.0000000000001679","journal-title":"Spine (Phila Pa 1976)"},{"issue":"3","key":"1517_CR10","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/s00586-018-05876-0","volume":"28","author":"T Bassani","year":"2019","unstructured":"Bassani T, Stucovitz E, Galbusera F, Brayda-Bruno M (2019) Is rasterstereography a valid noninvasive method for the screening of juvenile and adolescent idiopathic scoliosis? Eur Spine J 28(3):526\u2013535. https:\/\/doi.org\/10.1007\/s00586-018-05876-0","journal-title":"Eur Spine J"},{"key":"1517_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/s23208485","author":"B Mehta","year":"2023","unstructured":"Mehta B, Chockalingam N, Shannon T, Jevtic N, Lazic F, Jasani V et al (2023) Non-invasive assessment of back surface topography: technologies, techniques and clinical utility. Sensors (Basel). https:\/\/doi.org\/10.3390\/s23208485","journal-title":"Sensors (Basel)"},{"issue":"4","key":"1517_CR12","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1097\/00007632-198804000-00006","volume":"13","author":"I Weisz","year":"1988","unstructured":"Weisz I, Jefferson RJ, Turner-Smith AR, Houghton GR, Harris JD (1988) ISIS scanning: a useful assessment technique in the management of scoliosis. Spine (Phila Pa 1976) 13(4):405\u2013408. https:\/\/doi.org\/10.1097\/00007632-198804000-00006","journal-title":"Spine (Phila Pa 1976)"},{"key":"1517_CR13","first-page":"157","volume":"140","author":"A Zubovi\u0107","year":"2008","unstructured":"Zubovi\u0107 A, Davies N, Berryman F, Pynsent P, Quraishi N, Lavy C et al (2008) New method of scoliosis deformity assessment: ISIS2 system. Stud Health Technol Inform 140:157\u2013160","journal-title":"Stud Health Technol Inform"},{"issue":"11","key":"1517_CR14","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1097\/00007632-199706010-00010","volume":"22","author":"TN Theologis","year":"1997","unstructured":"Theologis TN, Fairbank JC, Turner-Smith AR, Pantazopoulos T (1997) Early detection of progression in adolescent idiopathic scoliosis by measurement of changes in back shape with the integrated shape imaging system scanner. Spine (Phila Pa 1976) 22(11):1223\u20131227. https:\/\/doi.org\/10.1097\/00007632-199706010-00010","journal-title":"Spine (Phila Pa 1976)"},{"key":"1517_CR15","doi-asserted-by":"publisher","first-page":"226","DOI":"10.2174\/1874325001206010226","volume":"6","author":"L Rankine","year":"2012","unstructured":"Rankine L, Liu XC, Tassone C, Lyon R, Tarima S, Thometz J (2012) Reproducibility of newly developed spinal topography measurements for scoliosis. Open Orthop J 6:226\u2013230. https:\/\/doi.org\/10.2174\/1874325001206010226","journal-title":"Open Orthop J"},{"issue":"26","key":"1517_CR16","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1097\/brs.0b013e3181cbcc10","volume":"35","author":"CF Lee","year":"2010","unstructured":"Lee CF, Fong DYT, Cheung KMC, Cheng JCY, Ng BKW, Lam TP et al (2010) Costs of school scoliosis screening. Spine 35(26):2266\u20132272. https:\/\/doi.org\/10.1097\/brs.0b013e3181cbcc10","journal-title":"Spine"},{"key":"1517_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-68937-z","author":"S Kato","year":"2024","unstructured":"Kato S, Maeda Y, Nagura T, Nakamura M, Watanabe K (2024) Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-024-68937-z","journal-title":"Sci Rep"},{"issue":"9","key":"1517_CR18","doi-asserted-by":"publisher","first-page":"5748","DOI":"10.1007\/s00330-024-10616-8","volume":"34","author":"JW Van Der Graaf","year":"2024","unstructured":"Van Der Graaf JW, Van Hooff ML, Van Ginneken B, Huisman M, Rutten M, Lamers D et al (2024) Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI. Eur Radiol 34(9):5748\u20135757. https:\/\/doi.org\/10.1007\/s00330-024-10616-8","journal-title":"Eur Radiol"},{"issue":"3","key":"1517_CR19","doi-asserted-by":"publisher","first-page":"202","DOI":"10.2519\/josptopen.2024.0718","volume":"2","author":"WW-Y Chan","year":"2024","unstructured":"Chan WW-Y, Fu S-N, Zheng Y-P, Parent EC, Cheung JPY, Zheng DKY et al (2024) A systematic review of machine learning models for predicting curve progression in teenagers with idiopathic scoliosis. JOSPT Open 2(3):202\u2013224. https:\/\/doi.org\/10.2519\/josptopen.2024.0718","journal-title":"JOSPT Open"},{"key":"1517_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/ISEC61299.2024.10665199","author":"JJ Hai","year":"2024","unstructured":"Hai JJ, Meng N, Zhao M, Cheung JP-Y, Zhang T (2024) AI powered mobile analysis of Scoliosis among children in Qinghai- Tibetan Plateau of China. IEEE. https:\/\/doi.org\/10.1109\/ISEC61299.2024.10665199","journal-title":"IEEE"},{"issue":"11","key":"1517_CR21","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1007\/s00586-023-08052-1","volume":"33","author":"M Minotti","year":"2024","unstructured":"Minotti M, Negrini S, Cina A, Galbusera F, Zaina F, Bassani T (2024) Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis. Eur Spine J 33(11):4164\u20134170. https:\/\/doi.org\/10.1007\/s00586-023-08052-1","journal-title":"Eur Spine J"},{"key":"1517_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/ICME55011.2023.00169","author":"Z Xu","year":"2023","unstructured":"Xu Z, Hu J, Gao Q, Hang D, Zhou Q, Guo S et al (2023) Development of deep learning algorithms for automated scoliosis and abnormal posture screening using 2D back image. IEEE. https:\/\/doi.org\/10.1109\/ICME55011.2023.00169","journal-title":"IEEE"},{"key":"1517_CR23","doi-asserted-by":"publisher","first-page":"102050","DOI":"10.1016\/j.eclinm.2023.102050","volume":"61","author":"N Meng","year":"2023","unstructured":"Meng N, Wong K-YK, Zhao M, Cheung JPY, Zhang T (2023) Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation. eClinicalMedicine 61:102050. https:\/\/doi.org\/10.1016\/j.eclinm.2023.102050","journal-title":"eClinicalMedicine"},{"issue":"4","key":"1517_CR24","doi-asserted-by":"publisher","first-page":"2062","DOI":"10.1177\/21925682241282581","volume":"15","author":"L Zhang","year":"2025","unstructured":"Zhang L, Pei B, Zhang S, Lu D, Xu Y, Huang X et al (2025) A new method for scoliosis screening incorporating deep learning with back images. Glob Spine J 15(4):2062\u20132074. https:\/\/doi.org\/10.1177\/21925682241282581","journal-title":"Glob Spine J"},{"issue":"10","key":"1517_CR25","doi-asserted-by":"publisher","first-page":"8899","DOI":"10.1016\/j.jksuci.2022.08.019","volume":"34","author":"N Sabri","year":"2022","unstructured":"Sabri N, Hamed HNA, Ibrahim Z, Ibrahim K, Isa MA, Diah NM (2022) The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using evolving spiking neural network. J King Saud Univ Comput Inf Sci 34(10):8899\u20138908. https:\/\/doi.org\/10.1016\/j.jksuci.2022.08.019","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"3","key":"1517_CR26","doi-asserted-by":"publisher","first-page":"5559","DOI":"10.32604\/cmc.2022.029221","volume":"73","author":"N Sabri","year":"2022","unstructured":"Sabri N, Nuzly Abdull Hamed H, Ibrahim Z, Ibrahim K, Adham Isa M (2022) Integrated evolving spiking neural network and feature extraction methods for scoliosis classification. Comput Mater Contin 73(3):5559\u20135573. https:\/\/doi.org\/10.32604\/cmc.2022.029221","journal-title":"Comput Mater Contin"},{"key":"1517_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-23198-8_30","author":"Z Xu","year":"2022","unstructured":"Xu Z, Ouyang J, Gao Q, Gan A, Zhou Q, Hu J et al (2022) 2D photogrammetry image of adolescent idiopathic scoliosis screening using deep learning. Springer Nat Switz. https:\/\/doi.org\/10.1007\/978-3-031-23198-8_30","journal-title":"Springer Nat Switz"},{"key":"1517_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/IBITeC53045.2021.9649200","author":"J Arivudaiyanambi","year":"2021","unstructured":"Arivudaiyanambi J, Renganathan G, Cukovic S (2021) Classification of markerless 3D dorsal shapes in adolescent idiopathic scoliosis patients using machine learning approach. IEEE. https:\/\/doi.org\/10.1109\/IBITeC53045.2021.9649200","journal-title":"IEEE"},{"key":"1517_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-019-0635-8","author":"J Yang","year":"2019","unstructured":"Yang J, Zhang K, Fan H, Huang Z, Xiang Y, Yang J et al (2019) Development and validation of deep learning algorithms for scoliosis screening using back images. Commun Biol. https:\/\/doi.org\/10.1038\/s42003-019-0635-8","journal-title":"Commun Biol"},{"key":"1517_CR30","doi-asserted-by":"publisher","unstructured":"Bergeron C, Labelle H, Ronsky J, Zernicke R. Robust prediction of three-dimensional spinal curve from back surface for non-invasive follow-up of scoliosis. SPIE. https:\/\/doi.org\/10.1117\/12.595655","DOI":"10.1117\/12.595655"},{"issue":"1","key":"1517_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.artmed.2012.07.002","volume":"56","author":"MM Adankon","year":"2012","unstructured":"Adankon MM, Dansereau J, Labelle H, Cheriet F (2012) Non invasive classification system of scoliosis curve types using least-squares support vector machines. Artif Intell Med 56(1):99\u2013107. https:\/\/doi.org\/10.1016\/j.artmed.2012.07.002","journal-title":"Artif Intell Med"},{"issue":"14","key":"1517_CR32","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1097\/00007632-200107150-00017","volume":"26","author":"JL Jaremko","year":"2001","unstructured":"Jaremko JL, Poncet P, Ronsky J, Harder J, Dansereau J, Labelle H et al (2001) Estimation of spinal deformity in scoliosis from torso surface cross sections. Spine (Phila Pa 1976) 26(14):1583\u20131591. https:\/\/doi.org\/10.1097\/00007632-200107150-00017","journal-title":"Spine (Phila Pa 1976)"},{"key":"1517_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-92551-2","author":"N Mohamed","year":"2025","unstructured":"Mohamed N, Ruiz JMG, Hassan M, Costa OA, Burke TN, Mei Q et al (2025) Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-025-92551-2","journal-title":"Sci Rep"},{"key":"1517_CR34","doi-asserted-by":"publisher","first-page":"102779","DOI":"10.1016\/j.eclinm.2024.102779","volume":"75","author":"Z He","year":"2024","unstructured":"He Z, Lu N, Chen Y, Chun-Sing Chui E, Liu Z, Qin X et al (2024) Conditional generative adversarial network-assisted system for radiation-free evaluation of scoliosis using a single smartphone photograph: a model development and validation study. eClinicalMedicine 75:102779. https:\/\/doi.org\/10.1016\/j.eclinm.2024.102779","journal-title":"eClinicalMedicine"},{"key":"1517_CR35","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-031-55315-8_18","volume-title":"Computer methods in biomechanics and biomedical engineering II","author":"N Comte","year":"2024","unstructured":"Comte N, Pujades S, Courvoisier A, Daniel O, Franco J-S, Faure F et al (2024) 3D Inference of the scoliotic spine from depth maps of the back. In: Skalli W, Laporte S, Benoit A (eds) Computer methods in biomechanics and biomedical engineering II. Springer Nature Switzerland, Cham, pp 159\u2013168"},{"key":"1517_CR36","unstructured":"Department of Health, Student Health Service (2024) Student Health Service Annual Health Report for 2023\/24 School Year. Hong Kong: Department of Health. https:\/\/www.studenthealth.gov.hk\/english\/annual_health\/files\/student_health_service_annual_health_report_for_2023_24_school_year.pdf"},{"issue":"4","key":"1517_CR37","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3390\/info15040235","volume":"15","author":"R Guido","year":"2024","unstructured":"Guido R, Ferrisi S, Lofaro D, Conforti D (2024) An overview on the advancements of support vector machine models in healthcare applications: a review. Information (Basel) 15(4):235. https:\/\/doi.org\/10.3390\/info15040235","journal-title":"Information (Basel)"},{"key":"1517_CR38","doi-asserted-by":"publisher","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","volume":"1168","author":"X Ying","year":"2019","unstructured":"Ying X (2019) An overview of overfitting and its solutions. J Phys Conf Ser 1168:022022. https:\/\/doi.org\/10.1088\/1742-6596\/1168\/2\/022022","journal-title":"J Phys Conf Ser"},{"issue":"4","key":"1517_CR39","doi-asserted-by":"publisher","first-page":"697","DOI":"10.14245\/ns.1938426.213","volume":"16","author":"K Watanabe","year":"2019","unstructured":"Watanabe K, Aoki Y, Matsumoto M (2019) An application of artificial intelligence to diagnostic imaging of spine disease: estimating spinal alignment from moir\u00e9 images. Neurospine 16(4):697\u2013702. https:\/\/doi.org\/10.14245\/ns.1938426.213","journal-title":"Neurospine"},{"issue":"12","key":"1517_CR40","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1007\/s11517-020-02258-x","volume":"58","author":"S Rothstock","year":"2020","unstructured":"Rothstock S, Weiss H-R, Krueger D, Paul L (2020) Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data. Med Biol Eng Comput 58(12):2953\u20132962. https:\/\/doi.org\/10.1007\/s11517-020-02258-x","journal-title":"Med Biol Eng Comput"},{"issue":"23","key":"1517_CR41","doi-asserted-by":"publisher","first-page":"7382","DOI":"10.3390\/jcm12237382","volume":"12","author":"H Zhang","year":"2023","unstructured":"Zhang H, Huang C, Wang D, Li K, Han X, Chen X et al (2023) Artificial intelligence in scoliosis: current applications and future directions. J Clin Med 12(23):7382. https:\/\/doi.org\/10.3390\/jcm12237382","journal-title":"J Clin Med"},{"issue":"11","key":"1517_CR42","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1016\/j.spinee.2013.07.449","volume":"13","author":"P Phan","year":"2013","unstructured":"Phan P, Mezghani N, Wai EK, De Guise J, Labelle H (2013) Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification. Spine J 13(11):1527\u20131533. https:\/\/doi.org\/10.1016\/j.spinee.2013.07.449","journal-title":"Spine J"},{"key":"1517_CR43","doi-asserted-by":"publisher","DOI":"10.18502\/ijph.v50i11.7600","author":"DD Farhud","year":"2021","unstructured":"Farhud DD, Zokaei S (2021) Ethical issues of artificial intelligence in medicine and healthcare. Iran J Public Health. https:\/\/doi.org\/10.18502\/ijph.v50i11.7600","journal-title":"Iran J Public Health"},{"issue":"5","key":"1517_CR44","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1197\/jamia.m1279","volume":"10","author":"TL Tsai","year":"2003","unstructured":"Tsai TL, Fridsma DB, Gatti G (2003) Computer decision support as a source of interpretation error: the case of electrocardiograms. J Am Med Inform Assoc 10(5):478\u2013483. https:\/\/doi.org\/10.1197\/jamia.m1279","journal-title":"J Am Med Inform Assoc"},{"issue":"6","key":"1517_CR45","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1016\/j.jelectrocard.2018.08.007","volume":"51","author":"RR Bond","year":"2018","unstructured":"Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay D et al (2018) Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol 51(6):S6\u2013S11. https:\/\/doi.org\/10.1016\/j.jelectrocard.2018.08.007","journal-title":"J Electrocardiol"},{"issue":"4","key":"1517_CR46","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.jnlssr.2024.06.001","volume":"5","author":"M Abdelwanis","year":"2024","unstructured":"Abdelwanis M, Alarafati HK, Tammam MMS, Simsekler MCE (2024) Exploring the risks of automation bias in healthcare artificial intelligence applications: a bowtie analysis. J Safety Sci Resil 5(4):460\u2013469. https:\/\/doi.org\/10.1016\/j.jnlssr.2024.06.001","journal-title":"J Safety Sci Resil"},{"issue":"1","key":"1517_CR47","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.imed.2023.08.001","volume":"4","author":"H Xu","year":"2024","unstructured":"Xu H, Shuttleworth KMJ (2024) Medical artificial intelligence and the black box problem: a view based on the ethical principle of \u201cdo no harm.\u201d Intell Med 4(1):52\u201357. https:\/\/doi.org\/10.1016\/j.imed.2023.08.001","journal-title":"Intell Med"},{"key":"1517_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17431-x","author":"SM Lauritsen","year":"2020","unstructured":"Lauritsen SM, Kristensen M, Olsen MV, Larsen MS, Lauritsen KM, J\u00f8rgensen MJ et al (2020) Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-020-17431-x","journal-title":"Nat Commun"},{"key":"1517_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jclinepi.2017.12.018","volume":"96","author":"B Speich","year":"2018","unstructured":"Speich B, von Niederh\u00e4usern B, Schur N, Hemkens LG, F\u00fcrst T, Bhatnagar N, Alturki R, Agarwal A, Kasenda B, Pauli-Magnus C, Schwenkglenks M, Briel M, MAking Randomized Trials Affordable (MARTA) Group (2018) Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data. J Clin Epidemiol 96:1\u201311. https:\/\/doi.org\/10.1016\/j.jclinepi.2017.12.018","journal-title":"J Clin Epidemiol"},{"issue":"26","key":"1517_CR50","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1097\/BRS.0b013e3181cbcc10","volume":"35","author":"CF Lee","year":"2010","unstructured":"Lee CF, Fong DY, Cheung KM, Cheng JC, Ng BK, Lam TP, Mak KH, Yip PS, Luk KD (2010) Costs of school scoliosis screening: a large, population-based study. Spine (Phila Pa 1976) 35(26):2266\u20132272. https:\/\/doi.org\/10.1097\/BRS.0b013e3181cbcc10","journal-title":"Spine (Phila Pa 1976)"},{"issue":"1","key":"1517_CR51","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1186\/s13018-025-05564-y","volume":"20","author":"X Duan","year":"2025","unstructured":"Duan X, Ma X, Zhu M, Wang L, You D, Deng L, Niu N (2025) Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network. J Orthop Surg Res 20(1):161. https:\/\/doi.org\/10.1186\/s13018-025-05564-y","journal-title":"J Orthop Surg Res"},{"issue":"6","key":"1517_CR52","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare13060600","volume":"13","author":"TB Grivas","year":"2025","unstructured":"Grivas TB, Vasiliadis ES, Mazioti C, Mamzeri A, Papagianni D, Katzouraki G et al (2025) To screen or not to screen: \u201cfalse positive\u201d cases\u2014can they be treated as definitely false? Properly selecting the screening age\u2011range groups in scoliosis screening programs. Healthcare (Basel) 13(6):600. https:\/\/doi.org\/10.3390\/healthcare13060600","journal-title":"Healthcare (Basel)"},{"key":"1517_CR53","doi-asserted-by":"publisher","DOI":"10.1186\/1748-7161-2-11","volume":"2","author":"TB Grivas","year":"2007","unstructured":"Grivas TB, Vasiliadis ES, Mihas C, Savvidou O (2007) The effect of growth on the correlation between the spinal and rib cage deformity: implications on idiopathic scoliosis pathogenesis. Scoliosis 2:11. https:\/\/doi.org\/10.1186\/1748-7161-2-11","journal-title":"Scoliosis"},{"issue":"1","key":"1517_CR54","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1111\/j.1651-2227.1993.tb12521.x","volume":"82","author":"M Nissinen","year":"1993","unstructured":"Nissinen M, Heli\u00f6vaara M, Ylikoski M, Poussa M (1993) Trunk asymmetry and screening for scoliosis: a longitudinal cohort study of pubertal schoolchildren. Acta Paediatr 82(1):77\u201382. https:\/\/doi.org\/10.1111\/j.1651-2227.1993.tb12521.x","journal-title":"Acta Paediatr"}],"container-title":["Spine Deformity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43390-026-01517-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43390-026-01517-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43390-026-01517-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T19:15:13Z","timestamp":1786475713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43390-026-01517-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":54,"alternative-id":["1517"],"URL":"https:\/\/doi.org\/10.1007\/s43390-026-01517-5","relation":{},"ISSN":["2212-134X","2212-1358"],"issn-type":[{"value":"2212-134X","type":"print"},{"value":"2212-1358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"assertion":[{"value":"2 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have nothing to declare.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}]}}