{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T13:39:02Z","timestamp":1790689142989,"version":"4.1.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Secretar\u00eda de Ciencias, Humanidades y Tecnolog\u00edas","award":["CF-2023-G-1396"],"award-info":[{"award-number":["CF-2023-G-1396"]}]},{"name":"Secretar\u00eda de Ciencias, Humanidades y Tecnolog\u00edas","award":["CF-2023-G-1396"],"award-info":[{"award-number":["CF-2023-G-1396"]}]},{"name":"Secretar\u00eda de Ciencias, Humanidades y Tecnolog\u00edas","award":["CF-2023-G-1396"],"award-info":[{"award-number":["CF-2023-G-1396"]}]},{"name":"Secretar\u00eda de Ciencias, Humanidades y Tecnolog\u00edas","award":["CF-2023-G-1396"],"award-info":[{"award-number":["CF-2023-G-1396"]}]},{"name":"Secretar\u00eda de Ciencias, Humanidades y Tecnolog\u00edas","award":["CF-2023-G-1396"],"award-info":[{"award-number":["CF-2023-G-1396"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clinic Rev Allerg Immunol"],"DOI":"10.1007\/s12016-025-09076-9","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T22:34:47Z","timestamp":1751927687000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrating Machine Learning into Myositis Research: a Systematic Review"],"prefix":"10.1007","volume":"68","author":[{"given":"Christian","family":"Juarez-Gomez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Aguilar-Vazquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emiliano","family":"Gonzalez-Gauna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela Paola","family":"Garcia-Ordo\u00f1ez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beatriz Teresita","family":"Martin-Marquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia-Alejandra","family":"Gomez-Rios","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Becerra-Jimenez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arahi","family":"Gaspar-Ruiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Vazquez-Del Mercado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"9076_CR1","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1136\/annrheumdis-2019-216599","volume":"79","author":"I Pinal-Fernandez","year":"2020","unstructured":"Pinal-Fernandez I, Casal-Dominguez M, Derfoul A et al (2020) Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis. Ann Rheum Dis 79:1234\u20131242. https:\/\/doi.org\/10.1136\/annrheumdis-2019-216599","journal-title":"Ann Rheum Dis"},{"key":"9076_CR2","doi-asserted-by":"publisher","unstructured":"Dalakas MC (2023) Autoimmune inflammatory myopathies. pp 425\u2013460. https:\/\/doi.org\/10.1016\/b978-0-323-98818-6.00023-6","DOI":"10.1016\/b978-0-323-98818-6.00023-6"},{"key":"9076_CR3","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1002\/art.40320","volume":"69","author":"IE Lundberg","year":"2017","unstructured":"Lundberg IE, Tj\u00e4rnlund A, Bottai M et al (2017) 2017 European League Against Rheumatism\/American College of Rheumatology Classification Criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Arthritis Rheumatol 69:2271\u20132282. https:\/\/doi.org\/10.1002\/art.40320","journal-title":"Arthritis Rheumatol"},{"key":"9076_CR4","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.nmd.2004.02.006","volume":"14","author":"JE Hoogendijk","year":"2004","unstructured":"Hoogendijk JE, Amato AA, Lecky BR et al (2004) 119th ENMC international workshop: trial design in adult idiopathic inflammatory myopathies, with the exception of inclusion body myositis, 10\u201312 October 2003, Naarden, The Netherlands. Neuromuscul Disord 14:337\u2013345. https:\/\/doi.org\/10.1016\/j.nmd.2004.02.006","journal-title":"Neuromuscul Disord"},{"key":"9076_CR5","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1002\/art.43038","volume":"77","author":"S Faghihi-Kashani","year":"2025","unstructured":"Faghihi-Kashani S, Yoshida A, Bozan F et al (2025) Clinical characteristics of anti-synthetase syndrome: analysis from the classification criteria for anti-synthetase syndrome project. Arthritis Rheumatol 77:477\u2013489. https:\/\/doi.org\/10.1002\/art.43038","journal-title":"Arthritis Rheumatol"},{"key":"9076_CR6","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1590\/S1806-37132011000100015","volume":"37","author":"J Solomon","year":"2011","unstructured":"Solomon J, Swigris JJ, Brown KK (2011) Doen\u00e7a pulmonar intersticial relacionada a miosite e a s\u00edndrome antissintetase. J Bras Pneumol 37:100\u2013109. https:\/\/doi.org\/10.1590\/S1806-37132011000100015","journal-title":"J Bras Pneumol"},{"key":"9076_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1111\/joim.12524","volume":"280","author":"IE Lundberg","year":"2016","unstructured":"Lundberg IE, Miller FW, Tj\u00e4rnlund A, Bottai M (2016) Diagnosis and classification of idiopathic inflammatory myopathies. J Intern Med 280:39\u201351. https:\/\/doi.org\/10.1111\/joim.12524","journal-title":"J Intern Med"},{"key":"9076_CR8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1111\/j.1756-185X.2010.01472.x","volume":"13","author":"S Cox","year":"2010","unstructured":"Cox S, Limaye V, Hill C et al (2010) Idiopathic inflammatory myopathies: diagnostic criteria, classification and epidemiological features. Int J Rheum Dis 13:117\u2013124. https:\/\/doi.org\/10.1111\/j.1756-185X.2010.01472.x","journal-title":"Int J Rheum Dis"},{"key":"9076_CR9","doi-asserted-by":"publisher","first-page":"17671","DOI":"10.1038\/s41598-022-22754-4","volume":"12","author":"A Aguilar-Vazquez","year":"2022","unstructured":"Aguilar-Vazquez A, Chavarria-Avila E, Salazar-Paramo M et al (2022) Impaired muscle strength is associated with ultrastructure damage in myositis. Sci Rep 12:17671. https:\/\/doi.org\/10.1038\/s41598-022-22754-4","journal-title":"Sci Rep"},{"key":"9076_CR10","doi-asserted-by":"publisher","first-page":"802499","DOI":"10.3389\/fimmu.2022.802499","volume":"13","author":"L Zhao","year":"2022","unstructured":"Zhao L, Xie S, Zhou B et al (2022) Machine learning algorithms identify clinical subtypes and cancer in anti-TIF1\u03b3+ myositis: a longitudinal study of 87 patients. Front Immunol 13:802499. https:\/\/doi.org\/10.3389\/fimmu.2022.802499","journal-title":"Front Immunol"},{"key":"9076_CR11","doi-asserted-by":"publisher","first-page":"103105","DOI":"10.1016\/j.autrev.2022.103105","volume":"21","author":"MG Danieli","year":"2022","unstructured":"Danieli MG, Tonacci A, Paladini A et al (2022) A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases. Autoimmun Rev 21:103105. https:\/\/doi.org\/10.1016\/j.autrev.2022.103105","journal-title":"Autoimmun Rev"},{"key":"9076_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15729-6","volume-title":"An introduction to machine learning","author":"G Rebala","year":"2019","unstructured":"Rebala G, Ravi A, Churiwala S (2019) An introduction to machine learning. Springer International Publishing, Cham"},{"key":"9076_CR13","doi-asserted-by":"publisher","unstructured":"Jovel J, Greiner R (2021) An introduction to machine learning approaches for biomedical research. Front Med (Lausanne) 8. https:\/\/doi.org\/10.3389\/fmed.2021.771607","DOI":"10.3389\/fmed.2021.771607"},{"key":"9076_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22475-2","volume-title":"Supervised and unsupervised learning for data science","author":"MW Berry","year":"2020","unstructured":"Berry MW, Mohamed A, Yap BW (2020) Supervised and unsupervised learning for data science. Springer International Publishing, Cham"},{"key":"9076_CR15","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1177\/0003489419863822","volume":"128","author":"A Walker","year":"2019","unstructured":"Walker A, Surda P (2019) Unsupervised learning techniques for the investigation of chronic rhinosinusitis. Ann Otol, Rhinol Laryngol 128:1170\u20131176. https:\/\/doi.org\/10.1177\/0003489419863822","journal-title":"Ann Otol, Rhinol Laryngol"},{"key":"9076_CR16","doi-asserted-by":"publisher","unstructured":"Majumder A (2021) Introduction to reinforcement learning. Deep reinforcement learning in unity. Apress, Berkeley, CA, pp 1\u201371.\u00a0https:\/\/doi.org\/10.1007\/978-1-4842-6503-1_1","DOI":"10.1007\/978-1-4842-6503-1_1"},{"key":"9076_CR17","doi-asserted-by":"publisher","first-page":"6086","DOI":"10.1038\/s41598-024-56706-x","volume":"14","author":"O Rainio","year":"2024","unstructured":"Rainio O, Teuho J, Kl\u00e9n R (2024) Evaluation metrics and statistical tests for machine learning. Sci Rep 14:6086. https:\/\/doi.org\/10.1038\/s41598-024-56706-x","journal-title":"Sci Rep"},{"key":"9076_CR18","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30:1145\u20131159. https:\/\/doi.org\/10.1016\/S0031-3203(96)00142-2","journal-title":"Pattern Recognit"},{"key":"9076_CR19","doi-asserted-by":"publisher","first-page":"5979","DOI":"10.1038\/s41598-022-09954-8","volume":"12","author":"SA Hicks","year":"2022","unstructured":"Hicks SA, Str\u00fcmke I, Thambawita V et al (2022) On evaluation metrics for medical applications of artificial intelligence. Sci Rep 12:5979. https:\/\/doi.org\/10.1038\/s41598-022-09954-8","journal-title":"Sci Rep"},{"key":"9076_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"9076_CR21","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s12016-020-08805-6","volume":"60","author":"M Jiang","year":"2021","unstructured":"Jiang M, Li Y, Jiang C et al (2021) Machine learning in rheumatic diseases. Clin Rev Allergy Immunol 60:96\u2013110. https:\/\/doi.org\/10.1007\/s12016-020-08805-6","journal-title":"Clin Rev Allergy Immunol"},{"key":"9076_CR22","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.2147\/JMDH.S410301","volume":"16","author":"B Zhang","year":"2023","unstructured":"Zhang B, Shi H, Wang H (2023) Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach. J Multidiscip Healthc 16:1779\u20131791. https:\/\/doi.org\/10.2147\/JMDH.S410301","journal-title":"J Multidiscip Healthc"},{"key":"9076_CR23","doi-asserted-by":"publisher","unstructured":"Yosinski J, Clune J, Nguyen A et al (2015) Understanding neural networks through deep visualization. arXiv (Cornell University). https:\/\/doi.org\/10.48550\/arxiv.1506.06579","DOI":"10.48550\/arxiv.1506.06579"},{"key":"9076_CR24","doi-asserted-by":"publisher","first-page":"e001063","DOI":"10.1136\/rmdopen-2019-001063","volume":"6","author":"B Stoel","year":"2020","unstructured":"Stoel B (2020) Use of artificial intelligence in imaging in rheumatology \u2013 current status and future perspectives. RMD Open 6:e001063. https:\/\/doi.org\/10.1136\/rmdopen-2019-001063","journal-title":"RMD Open"},{"key":"9076_CR25","doi-asserted-by":"publisher","first-page":"672008","DOI":"10.3389\/fimmu.2021.672008","volume":"12","author":"A Aguilar-Vazquez","year":"2021","unstructured":"Aguilar-Vazquez A, Chavarria-Avila E, Pizano-Martinez O et al (2021) Geographical latitude remains as an important factor for the prevalence of some myositis autoantibodies: a systematic review. Front Immunol 12:672008. https:\/\/doi.org\/10.3389\/fimmu.2021.672008","journal-title":"Front Immunol"},{"key":"9076_CR26","doi-asserted-by":"publisher","first-page":"e0184059","DOI":"10.1371\/journal.pone.0184059","volume":"12","author":"P Burlina","year":"2017","unstructured":"Burlina P, Billings S, Joshi N, Albayda J (2017) Automated diagnosis of myositis from muscle ultrasound: exploring the use of machine learning and deep learning methods. PLoS ONE 12:e0184059. https:\/\/doi.org\/10.1371\/journal.pone.0184059","journal-title":"PLoS ONE"},{"key":"9076_CR27","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.berh.2005.01.002","volume":"19","author":"W Grassi","year":"2005","unstructured":"Grassi W, Salaffi F, Filippucci E (2005) Ultrasound in rheumatology. Best Pract Res Clin Rheumatol 19:467\u2013485. https:\/\/doi.org\/10.1016\/j.berh.2005.01.002","journal-title":"Best Pract Res Clin Rheumatol"},{"key":"9076_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.compbiomed.2018.12.006","volume":"105","author":"P Burlina","year":"2019","unstructured":"Burlina P, Joshi N, Billings S et al (2019) Deep embeddings for novelty detection in myopathy. Comput Biol Med 105:46\u201353. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.12.006","journal-title":"Comput Biol Med"},{"key":"9076_CR29","doi-asserted-by":"publisher","first-page":"237","DOI":"10.2152\/jmi.66.237","volume":"66","author":"H Nodera","year":"2019","unstructured":"Nodera H, Sogawa K, Takamatsu N et al (2019) Texture analysis of sonographic muscle images can distinguish myopathic conditions. J Med Invest 66:237\u2013247. https:\/\/doi.org\/10.2152\/jmi.66.237","journal-title":"J Med Invest"},{"key":"9076_CR30","doi-asserted-by":"publisher","first-page":"1146015","DOI":"10.3389\/fneur.2023.1146015","volume":"14","author":"AS Zubair","year":"2023","unstructured":"Zubair AS, Salam S, Dimachkie MM et al (2023) Imaging biomarkers in the idiopathic inflammatory myopathies. Front Neurol 14:1146015. https:\/\/doi.org\/10.3389\/fneur.2023.1146015","journal-title":"Front Neurol"},{"key":"9076_CR31","doi-asserted-by":"publisher","first-page":"1455867","DOI":"10.3389\/fimmu.2025.1455867","volume":"16","author":"JJ Paik","year":"2025","unstructured":"Paik JJ, Christopher-Stine L, Boesen M et al (2025) The utility of muscle magnetic resonance imaging in idiopathic inflammatory myopathies: a scoping review. Front Immunol 16:1455867. https:\/\/doi.org\/10.3389\/fimmu.2025.1455867","journal-title":"Front Immunol"},{"key":"9076_CR32","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.diii.2022.01.012","volume":"103","author":"V Fabry","year":"2022","unstructured":"Fabry V, Mamalet F, Laforet A et al (2022) A deep learning tool without muscle-by-muscle grading to differentiate myositis from facio-scapulo-humeral dystrophy using MRI. Diagn Interv Imaging 103:353\u2013359. https:\/\/doi.org\/10.1016\/j.diii.2022.01.012","journal-title":"Diagn Interv Imaging"},{"key":"9076_CR33","doi-asserted-by":"publisher","first-page":"9821","DOI":"10.1038\/s41598-021-89311-3","volume":"11","author":"K Nagawa","year":"2021","unstructured":"Nagawa K, Suzuki M, Yamamoto Y et al (2021) Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies. Sci Rep 11:9821. https:\/\/doi.org\/10.1038\/s41598-021-89311-3","journal-title":"Sci Rep"},{"key":"9076_CR34","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29:102\u2013127. https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","journal-title":"Z Med Phys"},{"key":"9076_CR35","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s41374-021-00647-w","volume":"102","author":"Y Kabeya","year":"2022","unstructured":"Kabeya Y, Okubo M, Yonezawa S et al (2022) Deep convolutional neural network-based algorithm for muscle biopsy diagnosis. Lab Invest 102:220\u2013226. https:\/\/doi.org\/10.1038\/s41374-021-00647-w","journal-title":"Lab Invest"},{"key":"9076_CR36","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s41747-023-00350-z","volume":"7","author":"F Wang","year":"2023","unstructured":"Wang F, Fang S, Li J et al (2023) Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy. Eur Radiol Exp 7:51. https:\/\/doi.org\/10.1186\/s41747-023-00350-z","journal-title":"Eur Radiol Exp"},{"key":"9076_CR37","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1007\/s00330-022-09254-9","volume":"33","author":"F Wang","year":"2022","unstructured":"Wang F, Zhou S, Hou B et al (2022) Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation. Eur Radiol 33:2350\u20132357. https:\/\/doi.org\/10.1007\/s00330-022-09254-9","journal-title":"Eur Radiol"},{"key":"9076_CR38","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13000-025-01608-3","volume":"20","author":"A Massaro","year":"2025","unstructured":"Massaro A, Cazzato G, Ingravallo G et al (2025) Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies. Diagn Pathol 20:13. https:\/\/doi.org\/10.1186\/s13000-025-01608-3","journal-title":"Diagn Pathol"},{"key":"9076_CR39","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1093\/rheumatology\/keab166","volume":"60","author":"AGS Oldroyd","year":"2021","unstructured":"Oldroyd AGS, Allard AB, Callen JP et al (2021) A systematic review and meta-analysis to inform cancer screening guidelines in idiopathic inflammatory myopathies. Rheumatology 60:2615\u20132628. https:\/\/doi.org\/10.1093\/rheumatology\/keab166","journal-title":"Rheumatology"},{"key":"9076_CR40","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1038\/s41584-023-01045-w","volume":"19","author":"AGS Oldroyd","year":"2023","unstructured":"Oldroyd AGS, Callen JP, Chinoy H et al (2023) International Guideline for Idiopathic Inflammatory Myopathy-Associated Cancer Screening: an International Myositis Assessment and Clinical Studies Group (IMACS) initiative. Nat Rev Rheumatol 19:805\u2013817. https:\/\/doi.org\/10.1038\/s41584-023-01045-w","journal-title":"Nat Rev Rheumatol"},{"issue":"2","key":"9076_CR41","doi-asserted-by":"publisher","first-page":"330","DOI":"10.55563\/clinexprheumatol\/8ievtq","volume":"41","author":"W Zhang","year":"2023","unstructured":"Zhang W, Huang G, Zheng K et al (2023) Application of logistic regression and machine learning methods for idiopathic inflammatory myopathies malignancy prediction. Clin Exp Rheumatol 41(2):330\u2013339. https:\/\/doi.org\/10.55563\/clinexprheumatol\/8ievtq","journal-title":"Clin Exp Rheumatol"},{"key":"9076_CR42","doi-asserted-by":"publisher","first-page":"e62836","DOI":"10.2196\/62836","volume":"27","author":"H Li","year":"2025","unstructured":"Li H, Zou R, Xin H et al (2025) Mortality risk prediction in patients with antimelanoma differentiation-associated, gene 5 antibody-positive, dermatomyositis-associated interstitial lung disease: algorithm development and validation. J Med Internet Res 27:e62836. https:\/\/doi.org\/10.2196\/62836","journal-title":"J Med Internet Res"},{"key":"9076_CR43","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1183\/16000617.00002015","volume":"24","author":"J-C Lega","year":"2015","unstructured":"Lega J-C, Reynaud Q, Belot A et al (2015) Idiopathic inflammatory myopathies and the lung. Eur Respir Rev 24:216\u2013238. https:\/\/doi.org\/10.1183\/16000617.00002015","journal-title":"Eur Respir Rev"},{"key":"9076_CR44","doi-asserted-by":"publisher","first-page":"224","DOI":"10.55563\/clinexprheumatol\/i2xeao","volume":"40","author":"J Zhu","year":"2022","unstructured":"Zhu J, Wu L, Zhou Y et al (2022) A retrospective cohort study in Chinese patients with adult polymyositis and dermatomyositis: risk of comorbidities and subclassification using machine learning. Clin Exp Rheumatol 40:224\u2013236. https:\/\/doi.org\/10.55563\/clinexprheumatol\/i2xeao","journal-title":"Clin Exp Rheumatol"},{"key":"9076_CR45","doi-asserted-by":"publisher","first-page":"940802","DOI":"10.3389\/fimmu.2022.940802","volume":"13","author":"Y Xue","year":"2023","unstructured":"Xue Y, Zhang J, Li C et al (2023) Machine learning for screening and predicting the risk of anti-MDA5 antibody in juvenile dermatomyositis children. Front Immunol 13:940802. https:\/\/doi.org\/10.3389\/fimmu.2022.940802","journal-title":"Front Immunol"},{"key":"9076_CR46","doi-asserted-by":"publisher","first-page":"e3598","DOI":"10.1002\/jgm.3598","volume":"26","author":"H Liu","year":"2024","unstructured":"Liu H, Deng L, Guo Y et al (2024) Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune-mediated necrotizing myopathy. J Gene Med 26:e3598. https:\/\/doi.org\/10.1002\/jgm.3598","journal-title":"J Gene Med"},{"key":"9076_CR47","doi-asserted-by":"publisher","unstructured":"Zhang L, Fu L, Zhang G et al (2024) Clinico\u2010sero\u2010pathological profiles and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different perifascicular changes. CNS Neurosci Ther 30. https:\/\/doi.org\/10.1111\/cns.14882","DOI":"10.1111\/cns.14882"},{"key":"9076_CR48","doi-asserted-by":"publisher","first-page":"1429817","DOI":"10.3389\/fimmu.2024.1429817","volume":"15","author":"X Wang","year":"2024","unstructured":"Wang X, Hu H, Yan G et al (2024) Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning. Front Immunol 15:1429817. https:\/\/doi.org\/10.3389\/fimmu.2024.1429817","journal-title":"Front Immunol"},{"key":"9076_CR49","doi-asserted-by":"publisher","first-page":"103058","DOI":"10.1016\/j.arcmed.2024.103058","volume":"55","author":"Q Wu","year":"2024","unstructured":"Wu Q, Wang W, Qiu L et al (2024) Activity prediction modeling based on a combination of growth differentiation factor 15 and serum biomarker levels in dermatomyositis and polymyositis. Arch Med Res 55:103058. https:\/\/doi.org\/10.1016\/j.arcmed.2024.103058","journal-title":"Arch Med Res"},{"key":"9076_CR50","doi-asserted-by":"publisher","first-page":"103353","DOI":"10.1016\/j.autrev.2023.103353","volume":"22","author":"MG Danieli","year":"2023","unstructured":"Danieli MG, Paladini A, Longhi E et al (2023) A machine learning analysis to evaluate the outcome measures in inflammatory myopathies. Autoimmun Rev 22:103353. https:\/\/doi.org\/10.1016\/j.autrev.2023.103353","journal-title":"Autoimmun Rev"},{"key":"9076_CR51","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.clinph.2022.11.019","volume":"146","author":"MR Tannemaat","year":"2023","unstructured":"Tannemaat MR, Kefalas M, Geraedts VJ et al (2023) Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach. Clin Neurophysiol 146:49\u201354. https:\/\/doi.org\/10.1016\/j.clinph.2022.11.019","journal-title":"Clin Neurophysiol"},{"key":"9076_CR52","doi-asserted-by":"publisher","first-page":"107917","DOI":"10.1016\/j.cmpb.2023.107917","volume":"243","author":"R Ryypp\u00f6","year":"2024","unstructured":"Ryypp\u00f6 R, H\u00e4yrynen S, Joutsijoki H et al (2024) Comparison of machine learning methods in the early identification of vasculitides, myositides and glomerulonephritides. Comput Methods Programs Biomed 243:107917. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107917","journal-title":"Comput Methods Programs Biomed"}],"container-title":["Clinical Reviews in Allergy & Immunology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12016-025-09076-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12016-025-09076-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12016-025-09076-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T22:34:49Z","timestamp":1751927689000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12016-025-09076-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9076"],"URL":"https:\/\/doi.org\/10.1007\/s12016-025-09076-9","relation":{},"ISSN":["1559-0267"],"issn-type":[{"value":"1559-0267","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"27 June 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The manuscript was not submitted to other journals for consideration. The submitted work is a review with no plagiarism. The results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. No data, text, or theories by others are presented as if they were the author\u2019s own. Proper acknowledgements are given to the cited works.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"62"}}