{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:04:07Z","timestamp":1791666247428,"version":"4.3.4"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012041","name":"Versus Arthritis","doi-asserted-by":"publisher","award":["22467"],"award-info":[{"award-number":["22467"]}],"id":[{"id":"10.13039\/501100012041","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["MSCA-ITN-ETN-2020 GA: 955735"],"award-info":[{"award-number":["MSCA-ITN-ETN-2020 GA: 955735"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Spine J"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A PRISMA systematic review protocol was developed and four electronic databases and reference lists were searched. Strict inclusion and exclusion criteria were defined. A PROBAST risk of bias and applicability analysis was performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>1350 articles were extracted. Duplicates were removed and title and abstract searching identified original research articles that used machine learning (ML) algorithms to identify disc degeneration, herniation, bulge and Modic change from MRIs. 27 studies were included in the review; 25 and 14 studies were included multi-variate and bivariate meta-analysis, respectively. Studies used machine learning algorithms to assess LDD, disc herniation, bulge and Modic change. Models using deep learning, support vector machine, k-nearest neighbors, random forest and na\u00efve Bayes algorithms were included. Meta-analyses found no differences in algorithm or classification performance. When algorithms were tested in replication or external validation studies, they did not perform as well as when assessed in developmental studies. Data augmentation improved algorithm performance when compared to models used with smaller datasets, there were no performance differences between augmented data and large datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00586-023-07718-0","type":"journal-article","created":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T22:01:30Z","timestamp":1683496890000},"page":"3764-3787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7034-1092","authenticated-orcid":false,"given":"Roger","family":"Compte","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-3317","authenticated-orcid":false,"given":"Isabelle","family":"Granville Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5827-0562","authenticated-orcid":false,"given":"Amanda","family":"Isaac","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-7971","authenticated-orcid":false,"given":"Nathan","family":"Danckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0966-210X","authenticated-orcid":false,"given":"Terence","family":"McSweeney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7841-8568","authenticated-orcid":false,"given":"Panagiotis","family":"Liantis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2998-2744","authenticated-orcid":false,"given":"Frances M. K.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"issue":"10070","key":"7718_CR1","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/S0140-6736(16)30970-9","volume":"389","author":"C Maher","year":"2017","unstructured":"Maher C, Underwood M, Buchbinder R (2017) Non-specific low back pain. Lancet 389(10070):736\u2013747. https:\/\/doi.org\/10.1016\/S0140-6736(16)30970-9","journal-title":"Lancet"},{"issue":"2","key":"7718_CR2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1002\/art.20236","volume":"51","author":"AJ MacGregor","year":"2004","unstructured":"MacGregor AJ, Andrew T, Sambrook PN, Spector TD (2004) Structural, psychological, and genetic influences on low back and neck pain: A study of adult female twins. Arthritis Care Res (Hoboken) 51(2):160\u2013167. https:\/\/doi.org\/10.1002\/art.20236","journal-title":"Arthritis Care Res (Hoboken)"},{"key":"7718_CR3","unstructured":"National Institute for Health and Care Excellence Low back pain and sciatica in over 16s: assessment and management (NG59). http:\/\/www.nice.org.uk\/guidance\/ng59"},{"issue":"9","key":"7718_CR4","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/j.acra.2015.05.007","volume":"22","author":"RJ McDonald","year":"2015","unstructured":"McDonald RJ, Schwartz KM, Eckel LJ, Diehn FE, Hunt CH, Bartholmai BJ, Erickson BJ, Kallmes DF (2015) The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 22(9):1191\u20131198. https:\/\/doi.org\/10.1016\/j.acra.2015.05.007","journal-title":"Acad Radiol"},{"issue":"3","key":"7718_CR5","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1055\/s-0039-1684024","volume":"23","author":"A Hirschmann","year":"2019","unstructured":"Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P (2019) Artificial intelligence in musculoskeletal imaging: Review of current literature, challenges, and trends. Semin Musculoskelet Radiol 23(3):304\u2013311. https:\/\/doi.org\/10.1055\/s-0039-1684024","journal-title":"Semin Musculoskelet Radiol"},{"key":"7718_CR6","doi-asserted-by":"publisher","unstructured":"dos Santos IN, Pernambuco ML, da Silva AMB, Ruela G de A, de Oliveira AS (2021) Association between musculoskeletal pain and telework in the context of the COVID 19 pandemic: an integrative review. Rev Bras Med Trab 19(3):342\u2013350. https:\/\/doi.org\/10.47626\/1679-4435-2021-812","DOI":"10.47626\/1679-4435-2021-812"},{"issue":"7","key":"7718_CR7","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/s00586-009-1274-4","volume":"19","author":"LW Mysliwiec","year":"2010","unstructured":"Mysliwiec LW, Cholewicki J, Winkelpleck MD, Eis GP (2010) MSU Classification for herniated lumbar discs on MRI: Toward developing objective criteria for surgical selection. Eur Spine J 19(7):1087\u20131093. https:\/\/doi.org\/10.1007\/s00586-009-1274-4","journal-title":"Eur Spine J"},{"issue":"2","key":"7718_CR8","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jocn.2014.05.052","volume":"22","author":"RI Riesenburger","year":"2015","unstructured":"Riesenburger RI, Safain MG, Ogbuji R, Hayes J, Hwang SW (2015) A novel classification system of lumbar disc degeneration. J Clin Neurosci 22(2):346\u2013351. https:\/\/doi.org\/10.1016\/j.jocn.2014.05.052","journal-title":"J Clin Neurosci"},{"key":"7718_CR9","doi-asserted-by":"publisher","unstructured":"Guellil N, Argawal N, Krieghoff M, Kaden I, Hohaus C, Meisel HJ, Schenk P (2022) Novel methods to measure height and volume in healthy and degenerated lumbar discs in MRIs: A reliability assessment study. Diagnostics 12(6). https:\/\/doi.org\/10.3390\/diagnostics12061437","DOI":"10.3390\/diagnostics12061437"},{"issue":"2","key":"7718_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1148\/radiol.2302021289","volume":"230","author":"CWA Pfirrmann","year":"2004","unstructured":"Pfirrmann CWA, Dora C, Schmid MR, Zanetti M, Hodler J, Boos N (2004) MR image-based grading of lumbar nerve root compromise due to disk herniation: Reliability study with surgical correlation. Radiology 230(2):583\u2013588. https:\/\/doi.org\/10.1148\/radiol.2302021289","journal-title":"Radiology"},{"key":"7718_CR11","doi-asserted-by":"publisher","unstructured":"Harada GK, Siyaji ZK, Younis S, Louie PK, Samartzis D, An HS (2020) Imaging in spine surgery: current concepts and future directions. Spine Surg Relat Res 4(2):99\u2013110. https:\/\/doi.org\/10.22603\/SSRR.2020-0011","DOI":"10.22603\/SSRR.2020-0011"},{"issue":"6","key":"7718_CR12","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1097\/00007632-200303150-00013","volume":"28","author":"T Videman","year":"2003","unstructured":"Videman T, Batti\u00e9 MC, Gibbons LE, Maravilla K, Manninen H, Kaprio J (2003) Associations between back pain history and lumbar MRI findings. Spine  (Phila Pa 1976)  28(6):582\u2013588. https:\/\/doi.org\/10.1097\/00007632-200303150-00013","journal-title":"Spine (Phila Pa 1976)"},{"key":"7718_CR13","doi-asserted-by":"publisher","unstructured":"Modic MT, Steinberg PM, Ross JS, Masaryk TJ, Carter JR (1988) Degenerative disk disease: Assessment of changes in vertebral body marrow with MR imaging. Radiology 166(1 I):193\u2013199. https:\/\/doi.org\/10.1148\/radiology.166.1.3336678","DOI":"10.1148\/radiology.166.1.3336678"},{"issue":"1","key":"7718_CR14","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1148\/radiology.168.1.3289089","volume":"168","author":"MT Modic","year":"1988","unstructured":"Modic MT, Masaryk TJ, Ross JS, Carter JR (1988) Imaging of degenerative disk disease. Radiology 168(1):177\u2013186. https:\/\/doi.org\/10.1148\/radiology.168.1.3289089","journal-title":"Radiology"},{"issue":"9","key":"7718_CR15","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1007\/s00586-006-0185-x","volume":"15","author":"P Kjaer","year":"2006","unstructured":"Kjaer P, Korsholm L, Bendix T, Sorensen JS, Leboeuf-Yde C (2006) Modic changes and their associations with clinical findings. Eur Spine J 15(9):1312\u20131319. https:\/\/doi.org\/10.1007\/s00586-006-0185-x","journal-title":"Eur Spine J"},{"issue":"11","key":"7718_CR16","doi-asserted-by":"publisher","first-page":"3723","DOI":"10.1007\/s00586-016-4459-7","volume":"25","author":"S Dudli","year":"2016","unstructured":"Dudli S, Fields AJ, Samartzis D, Karppinen J, Lotz JC (2016) Pathobiology of Modic changes. Eur Spine J 25(11):3723\u20133734. https:\/\/doi.org\/10.1007\/s00586-016-4459-7","journal-title":"Eur Spine J"},{"key":"7718_CR17","doi-asserted-by":"crossref","unstructured":"M\u00e4\u00e4tt\u00e4 JH, Wadge S, MacGregor A, Karppinen J, Williams FMK (2015) ISSLS PRIZE WINNER: Vertebral endplate (Modic) change is an independent risk factor for episodes of severe and disabling low back pain. Spine  (Phila Pa 1976) 40(15). https:\/\/journals.lww.com\/spinejournal\/Fulltext\/2015\/08010\/ISSLS_Prize_Winner__Vertebral_Endplate__Modic_.10.aspx","DOI":"10.1097\/BRS.0000000000000937"},{"issue":"4","key":"7718_CR18","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.spinee.2014.11.012","volume":"15","author":"M Teraguchi","year":"2015","unstructured":"Teraguchi M, Yoshimura N, Hashizume H, Muraki S, Yamada H, Oka H, Minamide A, Nakagawa H, Ishimoto Y, Nagata K, Kagotani R, Tanaka S, Kawaguchi H, Nakamura K, Akune T, Yoshida M (2015) The association of combination of disc degeneration, end plate signal change, and Schmorl node with low back pain in a large population study: the Wakayama Spine Study. Spine J 15(4):622\u2013628. https:\/\/doi.org\/10.1016\/j.spinee.2014.11.012","journal-title":"Spine J"},{"issue":"2","key":"7718_CR19","doi-asserted-by":"publisher","first-page":"97","DOI":"10.5387\/fms.2022-17","volume":"68","author":"T Watanabe","year":"2022","unstructured":"Watanabe T, Otani K, Sekiguchi M, Konno S-I (2022) Relationship between lumbar disc degeneration on MRI and low back pain: A cross-sectional community study. Fukushima J Med Sci 68(2):97\u2013107. https:\/\/doi.org\/10.5387\/fms.2022-17","journal-title":"Fukushima J Med Sci"},{"issue":"4","key":"7718_CR20","doi-asserted-by":"publisher","first-page":"811","DOI":"10.3174\/ajnr.A4173","volume":"36","author":"W Brinjikji","year":"2015","unstructured":"Brinjikji W, Luetmer PH, Comstock B, Bresnahan BW, Chen LE, Deyo RA, Halabi S, Turner JA, Avins AL, James K, Wald JT, Kallmes DF, Jarvik JG (2015) Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. Am J Neuroradiol 36(4):811\u2013816. https:\/\/doi.org\/10.3174\/ajnr.A4173","journal-title":"Am J Neuroradiol"},{"issue":"1","key":"7718_CR21","doi-asserted-by":"publisher","first-page":"299","DOI":"10.2214\/ajr.184.1.01840299","volume":"184","author":"JC van Rijn","year":"2005","unstructured":"van Rijn JC, Klemets\u00f6 N, Reitsma JB, Majoie CBLM, Hulsmans FJ, Peul WC, Stam J, Bossuyt PM, den Heeten GJ (2005) Observer variation in MRI evaluation of patients suspected of lumbar disk herniation. Am J Roentgenol 184(1):299\u2013303. https:\/\/doi.org\/10.2214\/ajr.184.1.01840299","journal-title":"Am J Roentgenol"},{"issue":"4","key":"7718_CR22","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.spinee.2016.11.009","volume":"17","author":"R Herzog","year":"2017","unstructured":"Herzog R, Elgort DR, Flanders AE, Moley PJ (2017) Variability in diagnostic error rates of 10 MRI centers performing lumbar spine MRI examinations on the same patient within a 3-week period. Spine J 17(4):554\u2013561. https:\/\/doi.org\/10.1016\/j.spinee.2016.11.009","journal-title":"Spine J"},{"issue":"1","key":"7718_CR23","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.spinee.2015.08.026","volume":"16","author":"MC Fu","year":"2016","unstructured":"Fu MC, Webb ML, Buerba RA, Neway WE, Brown JE, Trivedi M, Lischuk AW, Haims AH, Grauer JN (2016) Comparison of agreement of cervical spine degenerative pathology findings in magnetic resonance imaging studies. Spine J 16(1):42\u201348. https:\/\/doi.org\/10.1016\/j.spinee.2015.08.026","journal-title":"Spine J"},{"issue":"17","key":"7718_CR24","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1097\/00007632-200109010-00011","volume":"26","author":"CW Pfirrmann","year":"2001","unstructured":"Pfirrmann CW, Metzdorf A, Zanetti M, Hodler J, Boos N (2001) Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine (Phila Pa 1976) 26(17):1873\u20131878. https:\/\/doi.org\/10.1097\/00007632-200109010-00011","journal-title":"Spine (Phila Pa 1976)"},{"issue":"6","key":"7718_CR25","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1007\/s11999-014-3674-y","volume":"473","author":"Y Li","year":"2015","unstructured":"Li Y, Fredrickson V, Resnick DK (2015) How should we grade lumbar disc herniation and nerve root compression? A systematic review. Clin Orthop Relat Res 473(6):1896\u20131902. https:\/\/doi.org\/10.1007\/s11999-014-3674-y","journal-title":"Clin Orthop Relat Res"},{"issue":"8","key":"7718_CR26","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1002\/jor.23311","volume":"34","author":"P Pandit","year":"2016","unstructured":"Pandit P, Talbott JF, Pedoia V, Dillon W, Majumdar S (2016) T1\u03c1 and T2-based characterization of regional variations in intervertebral discs to detect early degenerative changes. J Orthop Res 34(8):1373\u20131381. https:\/\/doi.org\/10.1002\/jor.23311","journal-title":"J Orthop Res"},{"key":"7718_CR27","doi-asserted-by":"publisher","unstructured":"Gao KT, Tibrewala R, Hess M, Bharadwaj UU, Inamdar G, Link TM, Chin CT, Pedoia V, Majumdar S (2022) Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning. JOR Spine 5(2):e1204. https:\/\/doi.org\/10.1002\/jsp2.1204","DOI":"10.1002\/jsp2.1204"},{"key":"7718_CR28","doi-asserted-by":"publisher","unstructured":"Castiglioni I, Rundo L, Codari M, di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D\u2019Amico NC, Sardanelli F (2021) AI applications to medical images: From machine learning to deep learning. Physica Medica, pp 839\u201324. https:\/\/doi.org\/10.1016\/j.ejmp.2021.02.006","DOI":"10.1016\/j.ejmp.2021.02.006"},{"key":"7718_CR29","doi-asserted-by":"publisher","unstructured":"Zhang L, Wang H (2021) A novel segmentation method for cervical vertebrae based on PointNet++ and converge segmentation. Comput Methods Programs Biomed 200105798. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105798","DOI":"10.1016\/j.cmpb.2020.105798"},{"key":"7718_CR30","doi-asserted-by":"publisher","unstructured":"Pang S, Pang C, Su Z, Lin L, Zhao L, Chen Y, Zhou Y, Lu H, Feng Q (2022) DGMSNet: Spine segmentation for MR image by a detection-guided mixed-supervised segmentation network. Med Image Anal 75102261. https:\/\/doi.org\/10.1016\/j.media.2021.102261","DOI":"10.1016\/j.media.2021.102261"},{"key":"7718_CR31","doi-asserted-by":"publisher","unstructured":"Li H, Luo H, Liu Y (2019) Paraspinal muscle segmentation based on deep neural network. Sensors (Switzerland) 19(12): https:\/\/doi.org\/10.3390\/s19122650","DOI":"10.3390\/s19122650"},{"issue":"9","key":"7718_CR32","doi-asserted-by":"publisher","first-page":"3249","DOI":"10.3390\/s21093249","volume":"21","author":"J Hwang","year":"2021","unstructured":"Hwang J, Hwang S (2021) Exploiting global structure information to improve medical image segmentation. Sensors 21(9):3249. https:\/\/doi.org\/10.3390\/s21093249","journal-title":"Sensors"},{"issue":"13","key":"7718_CR33","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1049\/iet-ipr.2019.0971","volume":"14","author":"L Silvoster","year":"2020","unstructured":"Silvoster L, Kumar RMS (2020) Efficient segmentation of lumbar intervertebral disc from MR images. IET Image Process 14(13):3243\u20133253. https:\/\/doi.org\/10.1049\/iet-ipr.2019.0971","journal-title":"IET Image Process"},{"key":"7718_CR34","doi-asserted-by":"publisher","unstructured":"Rehman F, Ali Shah SI, Riaz N, Gilani SO (2019) A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis. IEEE Access 7120387\u2013120398. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936492","DOI":"10.1109\/ACCESS.2019.2936492"},{"issue":"5","key":"7718_CR35","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.compmedimag.2009.02.006","volume":"33","author":"Y Kim","year":"2009","unstructured":"Kim Y, Kim D (2009) A fully automatic vertebra segmentation method using 3D deformable fences. Comput Med Imaging Graph 33(5):343\u2013352. https:\/\/doi.org\/10.1016\/j.compmedimag.2009.02.006","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"7718_CR36","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s00256-019-03342-6","volume":"49","author":"S Lee","year":"2020","unstructured":"Lee S, Choe EK, Kang HY, Yoon JW, Kim HS (2020) The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population. Skeletal Radiol 49(4):613\u2013618. https:\/\/doi.org\/10.1007\/s00256-019-03342-6","journal-title":"Skeletal Radiol"},{"issue":"1","key":"7718_CR37","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1109\/TBME.2007.894831","volume":"55","author":"H Lin","year":"2008","unstructured":"Lin H (2008) Identification of spinal deformity classification with total curvature analysis and artificial neural network. IEEE Trans Biomed Eng 55(1):376\u2013382. https:\/\/doi.org\/10.1109\/TBME.2007.894831","journal-title":"IEEE Trans Biomed Eng"},{"issue":"5","key":"7718_CR38","doi-asserted-by":"publisher","first-page":"E11","DOI":"10.3171\/2018.8.FOCUS18246","volume":"45","author":"JK Scheer","year":"2018","unstructured":"Scheer JK, Oh T, Smith JS, Shaffrey CI, Daniels AH, Sciubba DM, Hamilton DK, Protopsaltis TS, Passias PG, Hart RA, Burton DC, Bess S, Lafage R, Lafage V, Schwab F, Klineberg EO, Ames CP (2018) Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients. Neurosurg Focus 45(5):E11. https:\/\/doi.org\/10.3171\/2018.8.FOCUS18246","journal-title":"Neurosurg Focus"},{"key":"7718_CR39","doi-asserted-by":"publisher","unstructured":"Arvind V, Kim JS, Oermann EK, Kaji D, Cho SK (2018) Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using machine learning. Neurospine 15(4):329\u2013337. https:\/\/doi.org\/10.14245\/ns.1836248.124","DOI":"10.14245\/ns.1836248.124"},{"key":"7718_CR40","unstructured":"CoLumbo AI spine assistant receives an FDA 510(K) clearance. https:\/\/columbo.me\/columbo-ai-spine-assistant-receives-an-fda-510k-clearance\/ (Jan. 21, 2023)."},{"issue":"2","key":"7718_CR41","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1111\/1754-9485.13379","volume":"66","author":"SK Goergen","year":"2022","unstructured":"Goergen SK, Frazer HML, Reddy S (2022) Quality use of artificial intelligence in medical imaging: What do radiologists need to know? J Med Imaging Radiat Oncol 66(2):225\u2013232. https:\/\/doi.org\/10.1111\/1754-9485.13379","journal-title":"J Med Imaging Radiat Oncol"},{"key":"7718_CR42","doi-asserted-by":"publisher","unstructured":"Soin A, Merkow J, Long J, Cohen JP, Saligrama S, Kaiser S, Borg S, Tarapov I, Lungren MP (2022) CheXstray: Real-time multi-modal data concordance for drift detection in medical imaging AI. https:\/\/doi.org\/10.48550\/arxiv.2202.02833","DOI":"10.48550\/arxiv.2202.02833"},{"issue":"1","key":"7718_CR43","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170(1):51\u201358. https:\/\/doi.org\/10.7326\/M18-1376","journal-title":"Ann Intern Med"},{"key":"7718_CR44","doi-asserted-by":"publisher","unstructured":"Pan Q, Zhang K, He L, Dong Z, Zhang L, Wu X, Wu Y, Gao Y (2021) Automatically diagnosing disk bulge and disk herniation with lumbar magnetic resonance images by using deep convolutional neural networks: Method development study. JMIR Med Inform 9(5). https:\/\/doi.org\/10.2196\/14755","DOI":"10.2196\/14755"},{"issue":"1","key":"7718_CR45","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1038\/s41467-022-28387-5","volume":"13","author":"HD Zheng","year":"2022","unstructured":"Zheng HD, Sun YL, Kong DW, Yin MC, Chen J, Lin YP, Ma XF, Wang HS, Yuan GJ, Yao M, Cui XJ, Tian YZ, Wang YJ (2022) Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI. Nat Commun 13(1):841. https:\/\/doi.org\/10.1038\/s41467-022-28387-5","journal-title":"Nat Commun"},{"issue":"10","key":"7718_CR46","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","volume":"58","author":"JB Reitsma","year":"2005","unstructured":"Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58(10):982\u2013990. https:\/\/doi.org\/10.1016\/j.jclinepi.2005.02.022","journal-title":"J Clin Epidemiol"},{"key":"7718_CR47","doi-asserted-by":"publisher","unstructured":"Su ZH, Liu J, Yang MS, Chen ZY, You K, Shen J, Huang CJ, Zhao QH, Liu EQ, Zhao L, Feng QJ, Pang SM, Li SL, Lu H (2022) Automatic grading of disc herniation, central canal stenosis and nerve roots compression in lumbar magnetic resonance image diagnosis. Front Endocrinol (Lausanne) 13. https:\/\/doi.org\/10.3389\/fendo.2022.890371","DOI":"10.3389\/fendo.2022.890371"},{"issue":"8","key":"7718_CR48","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1007\/s00586-022-07311-x","volume":"31","author":"A Grob","year":"2022","unstructured":"Grob A, Loibl M, Jamaludin A, Winklhofer S, Fairbank JCT, Fekete T, Porchet F, Mannion AF (2022) External validation of the deep learning system \u2018SpineNet\u2019 for grading radiological features of degeneration on MRIs of the lumbar spine. Eur Spine J 31(8):2137\u20132148. https:\/\/doi.org\/10.1007\/s00586-022-07311-x","journal-title":"Eur Spine J"},{"issue":"5","key":"7718_CR49","doi-asserted-by":"publisher","first-page":"902","DOI":"10.3390\/diagnostics11050902","volume":"11","author":"NC Lehnen","year":"2021","unstructured":"Lehnen NC, Haase R, Faber J, R\u00fcber T, Vatter H, Radbruch A, Schmeel FC (2021) Detection of degenerative changes on MR images of the lumbar spine with a convolutional neural network: A feasibility study. Diagnostics 11(5):902. https:\/\/doi.org\/10.3390\/diagnostics11050902","journal-title":"Diagnostics"},{"key":"7718_CR50","doi-asserted-by":"publisher","unstructured":"McSweeney TP, Saarakkala S, Tiulpin A, Jamaludin A, Kadir T, Niinim\u00e4ki J, Karppinen J, M\u00e4\u00e4tt\u00e4 J (2022) External validation of Spinenet, a deep learning model for automated grading of lumbar disc degeneration MRI features, using the Northern Finland Birth Cohort. Osteoarthritis Cartilage 30(Supplementary 1):S86. https:\/\/doi.org\/10.1016\/j.joca.2022.02.106","DOI":"10.1016\/j.joca.2022.02.106"},{"issue":"20","key":"7718_CR51","doi-asserted-by":"publisher","first-page":"27215","DOI":"10.1007\/s11042-018-5914-8","volume":"77","author":"A Beulah","year":"2018","unstructured":"Beulah A, Sharmila TS, Pramod VK (2018) Disc bulge diagnostic model in axial lumbar MR images using Intervertebral disc Descriptor (IdD). Multimed Tools Appl 77(20):27215\u201327230. https:\/\/doi.org\/10.1007\/s11042-018-5914-8","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"7718_CR52","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.1007\/s00586-016-4654-6","volume":"25","author":"I Castro-Mateos","year":"2016","unstructured":"Castro-Mateos I, Hua R, Pozo JM, Lazary A, Frangi AF (2016) Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur Spine J 25(9):2721\u20132727. https:\/\/doi.org\/10.1007\/s00586-016-4654-6","journal-title":"Eur Spine J"},{"issue":"3","key":"7718_CR53","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1002\/jmri.27400","volume":"53","author":"F Gao","year":"2021","unstructured":"Gao F, Liu S, Zhang X, Wang X, Zhang J (2021) Automated grading of lumbar disc degeneration using a push-pull regularization network based on MRI. J Magn Reson Imaging 53(3):799\u2013806. https:\/\/doi.org\/10.1002\/jmri.27400","journal-title":"J Magn Reson Imaging"},{"key":"7718_CR54","doi-asserted-by":"publisher","unstructured":"Gong H, Liu J, Li S, Chen B (2021) Axial-SpineGAN: Simultaneous segmentation and diagnosis of multiple spinal structures on axial magnetic resonance imaging images. Phys Med Biol 66(11):115014. https:\/\/doi.org\/10.1088\/1361-6560\/abfad9","DOI":"10.1088\/1361-6560\/abfad9"},{"issue":"1","key":"7718_CR55","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.bbe.2018.09.003","volume":"39","author":"JS Athertya","year":"2019","unstructured":"Athertya JS, Saravana Kumar G, Govindaraj J (2019) Detection of Modic changes in MR images of spine using local binary patterns. Biocybern Biomed Eng 39(1):17\u201329. https:\/\/doi.org\/10.1016\/j.bbe.2018.09.003","journal-title":"Biocybern Biomed Eng"},{"issue":"4","key":"7718_CR56","doi-asserted-by":"publisher","first-page":"45013","DOI":"10.1088\/2057-1976\/ac00d2","volume":"7","author":"JS Athertya","year":"2021","unstructured":"Athertya JS, Kumar GS (2021) Classification of certain vertebral degenerations using MRI image features. Biomed Phys Eng Express 7(4):45013. https:\/\/doi.org\/10.1088\/2057-1976\/ac00d2","journal-title":"Biomed Phys Eng Express"},{"issue":"8","key":"7718_CR57","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.1007\/s00371-021-02154-x","volume":"38","author":"A Beulah","year":"2022","unstructured":"Beulah A, Sharmila TS, Pramod VK (2022) Degenerative disc disease diagnosis from lumbar MR images using hybrid features. Visual Comp 38(8):2771\u20132783. https:\/\/doi.org\/10.1007\/s00371-021-02154-x","journal-title":"Visual Comp"},{"issue":"6","key":"7718_CR58","doi-asserted-by":"publisher","first-page":"1850044","DOI":"10.4015\/S1016237218500448","volume":"30","author":"E Ebrahimzadeh","year":"2018","unstructured":"Ebrahimzadeh E, Fayaz F, Nikravan M, Ahmadi F, Dolatabad MR (2018) Towards an automatic diagnosis system for lumbar disc herniation: The significance of local subset feature selection. Biomed Eng (Singapore) 30(6):1850044. https:\/\/doi.org\/10.4015\/S1016237218500448","journal-title":"Biomed Eng (Singapore)"},{"issue":"6","key":"7718_CR59","doi-asserted-by":"publisher","first-page":"1650042","DOI":"10.4015\/S1016237216500423","volume":"28","author":"M Nikravan","year":"2016","unstructured":"Nikravan M, Ebrahimzadeh E, Izadi MR, Mikaeili M (2016) Toward a computer aided diagnosis system for lumbar disc herniation disease based on MR images analysis. Biomed Eng (Singapore) 28(6):1650042. https:\/\/doi.org\/10.4015\/S1016237216500423","journal-title":"Biomed Eng (Singapore)"},{"key":"7718_CR60","doi-asserted-by":"publisher","unstructured":"Lewandrowski KU, Muraleedharan N, Eddy SA, Sobti V, Reece BD, Le\u00f3n JFR, Shah S (2020) Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging. Int J Spine Surg 14S86\u2013S97. https:\/\/doi.org\/10.14444\/7131","DOI":"10.14444\/7131"},{"issue":"2","key":"7718_CR61","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1097\/RLI.0000000000000709","volume":"56","author":"F Niemeyer","year":"2021","unstructured":"Niemeyer F, Galbusera F, Tao Y, Kienle A, Beer M, Wilke HJ (2021) A deep learning model for the accurate and reliable classification of disc degeneration based on MRI data. Invest Radiol 56(2):78\u201385. https:\/\/doi.org\/10.1097\/RLI.0000000000000709","journal-title":"Invest Radiol"},{"issue":"6","key":"7718_CR62","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s11548-012-0674-9","volume":"7","author":"J Koh","year":"2012","unstructured":"Koh J, Chaudhary V, Dhillon G (2012) Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier. Int J Comput Assist Radiol Surg 7(6):861\u2013869. https:\/\/doi.org\/10.1007\/s11548-012-0674-9","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"7718_CR63","doi-asserted-by":"publisher","unstructured":"Han Z, Wei B, Mercado A, Leung S, Li S (2018) Spine-GAN: Semantic segmentation of multiple spinal structures. Med Image Anal, pp 5023\u20135035. https:\/\/doi.org\/10.1016\/j.media.2018.08.005","DOI":"10.1016\/j.media.2018.08.005"},{"key":"7718_CR64","doi-asserted-by":"publisher","unstructured":"Ghosh S, Alomari RS, Chaudhary V, Dhillon G (2011) Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp 5068\u20135071. https:\/\/doi.org\/10.1109\/IEMBS.2011.6091255","DOI":"10.1109\/IEMBS.2011.6091255"},{"key":"7718_CR65","doi-asserted-by":"publisher","unstructured":"He X, Landis M, Leung S, Warrington J, Shmuilovich O, Li S (2017) Automated grading of lumbar disc degeneration via supervised distance metric learning. Medical Imaging 2017: Computer-Aided Diagnosis 101341013443. https:\/\/doi.org\/10.1117\/12.2253688","DOI":"10.1117\/12.2253688"},{"key":"7718_CR66","doi-asserted-by":"publisher","unstructured":"Jamaludin A, Kadir T, Zisserman A (2016) Automatic Modic changes classification in spinal MRI. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 940214\u201326. https:\/\/doi.org\/10.1007\/978-3-319-41827-8_2","DOI":"10.1007\/978-3-319-41827-8_2"},{"key":"7718_CR67","doi-asserted-by":"publisher","unstructured":"Tsai JY, Hung IYJ, Guo YL, Jan YK, Lin CY, Shih TTF, Chen B bin, Lung CW (2021) Lumbar disc herniation automatic detection in magnetic resonance imaging based on deep learning. Front Bioeng Biotechnol, 9. https:\/\/doi.org\/10.3389\/fbioe.2021.708137","DOI":"10.3389\/fbioe.2021.708137"},{"issue":"2","key":"7718_CR68","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1002\/ima.22374","volume":"30","author":"S Sundarsingh","year":"2020","unstructured":"Sundarsingh S, Kesavan R (2020) Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier. Int J Imaging Syst Technol 30(2):340\u2013347. https:\/\/doi.org\/10.1002\/ima.22374","journal-title":"Int J Imaging Syst Technol"},{"issue":"5","key":"7718_CR69","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1007\/s00586-017-4956-3","volume":"26","author":"A Jamaludin","year":"2017","unstructured":"Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Batti\u00e9 MC, Fairbank J, McCall I (2017) ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J 26(5):1374\u20131383. https:\/\/doi.org\/10.1007\/s00586-017-4956-3","journal-title":"Eur Spine J"},{"issue":"21\u201322","key":"7718_CR70","doi-asserted-by":"publisher","first-page":"15171","DOI":"10.1007\/s11042-018-7011-4","volume":"79","author":"B Hashia","year":"2020","unstructured":"Hashia B, Mir AH (2020) Texture features\u2019 based classification of MR images of normal and herniated intervertebral discs. Multimed Tools Appl 79(21\u201322):15171\u201315190. https:\/\/doi.org\/10.1007\/s11042-018-7011-4","journal-title":"Multimed Tools Appl"},{"key":"7718_CR71","unstructured":"Guo C, Pleiss G, Sun Y, Weinberger KQ (2017) On calibration of modern neural networks. 34th International Conference on Machine Learning, ICML 2017, pp 32130\u20132143.."},{"key":"7718_CR72","doi-asserted-by":"publisher","unstructured":"Jamaludin A, Kadir T, Zisserman A (2017) SpineNet: Automated classification and evidence visualization in spinal MRIs. Med Image Anal, pp 4163\u20134173. https:\/\/doi.org\/10.1016\/j.media.2017.07.002","DOI":"10.1016\/j.media.2017.07.002"},{"key":"7718_CR73","unstructured":"Windsor R, Jamaludin A, Kadir T, Zisserman A SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans. arXiv e-prints. arXiv:2205.01683, May 01, 2022. http:\/\/arxiv.org\/abs\/2205.01683"},{"issue":"7","key":"7718_CR74","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.compmedimag.2014.04.006","volume":"38","author":"AB Oktay","year":"2014","unstructured":"Oktay AB, Albayrak NB, Akgul YS (2014) Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Comput Med Imaging Graph 38(7):613\u2013619. https:\/\/doi.org\/10.1016\/j.compmedimag.2014.04.006","journal-title":"Comput Med Imaging Graph"},{"key":"7718_CR75","doi-asserted-by":"publisher","unstructured":"Galbusera F, Casaroli G, Bassani T (2019) Artificial intelligence and machine learning in spine research. JOR Spine 2(1):e1044. https:\/\/doi.org\/10.1002\/jsp2.1044","DOI":"10.1002\/jsp2.1044"},{"issue":"5","key":"7718_CR76","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","volume":"65","author":"P Chlap","year":"2021","unstructured":"Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A (2021) A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65(5):545\u2013563. https:\/\/doi.org\/10.1111\/1754-9485.13261","journal-title":"J Med Imaging Radiat Oncol"},{"key":"7718_CR77","doi-asserted-by":"publisher","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","DOI":"10.1186\/s40537-019-0197-0"},{"key":"7718_CR78","doi-asserted-by":"publisher","unstructured":"Nguyen KP, Fatt CC, Treacher A, Mellema C, Trivedi MH (2021) Anatomically informed data augmentation for functional MRI with applications to deep learning, pp 1\u201311. https:\/\/doi.org\/10.1117\/12.2548630.","DOI":"10.1117\/12.2548630"},{"key":"7718_CR79","doi-asserted-by":"publisher","unstructured":"Hornung AL, Hornung CM, Mallow GM, Barajas JN, Espinoza Or\u00edas AA, Galbusera F, Wilke H-J, Colman M, Phillips FM, An HS, Samartzis D (2022) Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. Eur Spine J 1\u201315. https:\/\/doi.org\/10.1007\/s00586-021-07108-4","DOI":"10.1007\/s00586-021-07108-4"},{"issue":"6","key":"7718_CR80","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu X et al (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health 1(6):e271\u2013e297","journal-title":"Lancet Digital Health"},{"key":"7718_CR81","doi-asserted-by":"publisher","unstructured":"Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D et al (2021) Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. Npj Digital Medicine. Nat Res.  https:\/\/doi.org\/10.1038\/s41746-021-00438-z","DOI":"10.1038\/s41746-021-00438-z"},{"issue":"6","key":"7718_CR82","doi-asserted-by":"publisher","first-page":"e012799","DOI":"10.1136\/bmjopen-2016-012799","volume":"2016","author":"JF Cohen","year":"2015","unstructured":"Cohen JF, Korevaar DA, Altman DG, et al (2016) STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 6:e012799. https:\/\/doi.org\/10.1136\/bmjopen-2016-012799","journal-title":"BMJ Open"},{"issue":"3","key":"7718_CR83","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3348\/kjr.2019.0025","volume":"20","author":"DW Kim","year":"2019","unstructured":"Kim DW, Jang HY, Kim KW, Shin Y, Park SH (2019) Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: Results from recently published papers. Korean J Radiol 20(3):405\u2013410. https:\/\/doi.org\/10.3348\/kjr.2019.0025","journal-title":"Korean J Radiol"},{"key":"7718_CR84","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1007\/s00586-015-4195-4","volume":"25","author":"D Steffens","year":"2016","unstructured":"Steffens D, Hancock MJ, Pereira LS, Kent PM, Latimer J, Maher CG (2016) Do MRI findings identify patients with low back pain or sciatica who respond better to particular interventions? A systematic review. Eur Spine J 25:1170\u20131187","journal-title":"Eur Spine J"}],"container-title":["European Spine Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00586-023-07718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00586-023-07718-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00586-023-07718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T22:03:28Z","timestamp":1698617008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00586-023-07718-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":84,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["7718"],"URL":"https:\/\/doi.org\/10.1007\/s00586-023-07718-0","relation":{"has-review":[{"id-type":"doi","id":"10.14293\/S2199-1006.1.SOR-MED.A10164619.v1.RUMGMG","asserted-by":"object"}]},"ISSN":["0940-6719","1432-0932"],"issn-type":[{"value":"0940-6719","type":"print"},{"value":"1432-0932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"28 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}