{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:51:15Z","timestamp":1791309075705,"version":"4.3.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neurol Ther"],"DOI":"10.1007\/s40120-026-01030-x","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:59:10Z","timestamp":1791305950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Gadolinium-Sparing Detection of Active Multiple Sclerosis Lesions: A Structured Narrative Review"],"prefix":"10.1007","author":[{"given":"Elham","family":"Moases Ghaffary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-2985","authenticated-orcid":false,"given":"Omid","family":"Mirmosayyeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeedeh","family":"Mirbagheri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"1030_CR1","doi-asserted-by":"publisher","DOI":"10.36922\/AN025440106","author":"EM Ghaffary","year":"2026","unstructured":"Ghaffary EM, Rostampour J, Mirmosayyeb O. Tracking neurodegeneration in multiple sclerosis: the role of optical coherence tomography, optical coherence tomography angiography, and artificial intelligence-based imaging. Adv Neurol. 2026. https:\/\/doi.org\/10.36922\/AN025440106.","journal-title":"Adv Neurol"},{"issue":"6","key":"1030_CR2","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1002\/jmri.29817","volume":"62","author":"R Elahi","year":"2025","unstructured":"Elahi R, Taremi S, Najafi A, Karimi H, Asadollahzadeh E, Sajedi SA, et al. Advanced MRI methods for diagnosis and monitoring of multiple sclerosis (MS). J Magn Reson Imaging. 2025;62(6):1546\u201378.","journal-title":"J Magn Reson Imaging"},{"key":"1030_CR3","unstructured":"Azzolini F. Accuracy of multiple sclerosis diagnostic criteria for detecting perivenular demyelination visualized by MRI and frequency of MS-mimicking diseases. 2024."},{"key":"1030_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-41207-4_5","volume-title":"Breast MRI for high-risk screening","author":"F Sardanelli","year":"2020","unstructured":"Sardanelli F, Schiaffino S, Cozzi A, Carbonaro LA. Gadolinium-based contrast agents for breast MRI and uncertainties about brain gadolinium retention. In: Breast MRI for high-risk screening. Springer; 2020. p. 63\u201382."},{"issue":"1","key":"1030_CR5","doi-asserted-by":"publisher","first-page":"134","DOI":"10.3348\/kjr.2018.0356","volume":"20","author":"JW Choi","year":"2019","unstructured":"Choi JW, Moon W-J. Gadolinium deposition in the brain: current updates. Korean J Radiol. 2019;20(1):134\u201347.","journal-title":"Korean J Radiol"},{"issue":"2","key":"1030_CR6","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ejrad.2008.01.025","volume":"66","author":"S Morcos","year":"2008","unstructured":"Morcos S. Extracellular gadolinium contrast agents: differences in stability. Eur J Radiol. 2008;66(2):175\u20139.","journal-title":"Eur J Radiol"},{"issue":"8","key":"1030_CR7","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/S1474-4422(21)00095-8","volume":"20","author":"MP Wattjes","year":"2021","unstructured":"Wattjes MP, Ciccarelli O, Reich DS, Banwell B, De Stefano N, Enzinger C, et al. 2021 MAGNIMS\u2013CMSC\u2013NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 2021;20(8):653\u201370.","journal-title":"Lancet Neurol"},{"issue":"11","key":"1030_CR8","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1007\/s00234-022-03019-3","volume":"64","author":"M Diaz-Hurtado","year":"2022","unstructured":"Diaz-Hurtado M, Mart\u00ednez-Heras E, Solana E, Casas-Roma J, Llufriu S, Kanber B, et al. Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review. Neuroradiology. 2022;64(11):2103\u201317.","journal-title":"Neuroradiology"},{"issue":"4","key":"1030_CR9","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s00234-011-0886-7","volume":"54","author":"D Mortazavi","year":"2012","unstructured":"Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 2012;54(4):299\u2013320.","journal-title":"Neuroradiology"},{"issue":"8","key":"1030_CR10","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529\u201336.","journal-title":"Ann Intern Med"},{"key":"1030_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119787","volume":"265","author":"B Caba","year":"2023","unstructured":"Caba B, Cafaro A, Lombard A, Arnold DL, Elliott C, Liu D, et al. Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning. Neuroimage. 2023;265:119787.","journal-title":"Neuroimage"},{"key":"1030_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.797586","volume":"9","author":"SP Thakur","year":"2022","unstructured":"Thakur SP, Schindler MK, Bilello M, Bakas S. Clinically deployed computational assessment of multiple sclerosis lesions. Front Med. 2022;9:797586.","journal-title":"Front Med"},{"key":"1030_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2024.105642","volume":"87","author":"A Amini","year":"2024","unstructured":"Amini A, Shayganfar A, Amini Z, Ostovar L, HajiAhmadi S, Chitsaz N, et al. Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: a multicenter study. Multiple Scler Relat Disord. 2024;87:105642.","journal-title":"Multiple Scler Relat Disord"},{"key":"1030_CR14","doi-asserted-by":"publisher","DOI":"10.5114\/pjr\/206986","volume":"90","author":"M Elhaie","year":"2025","unstructured":"Elhaie M, Etemadifar M, Adariani AR, Khorasani A, Shahbazi-Gahrouei D. Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: a complete analysis of T1, T2, FLAIR, DWI, and SWI features. Pol J Radiol. 2025;90:e394.","journal-title":"Pol J Radiol"},{"issue":"11","key":"1030_CR15","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1007\/s00330-020-06995-3","volume":"30","author":"G Caruana","year":"2020","unstructured":"Caruana G, Pessini LM, Cannella R, Salvaggio G, de Barros A, Salerno A, et al. Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions. Eur Radiol. 2020;30(11):6348\u201356.","journal-title":"Eur Radiol"},{"issue":"9","key":"1030_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255939","volume":"16","author":"S Gaj","year":"2021","unstructured":"Gaj S, Ontaneda D, Nakamura K. Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI. PLoS ONE. 2021;16(9):e0255939.","journal-title":"PLoS ONE"},{"issue":"5","key":"1030_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ace261","volume":"9","author":"B Khajetash","year":"2023","unstructured":"Khajetash B, Talebi A, Bagherpour Z, Abbaspour S, Tavakoli M. Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images. Biomed Phys Eng Express. 2023;9(5):055004.","journal-title":"Biomed Phys Eng Express"},{"issue":"1","key":"1030_CR18","doi-asserted-by":"publisher","first-page":"16304","DOI":"10.1038\/s41598-024-67130-6","volume":"14","author":"M Naeeni Davarani","year":"2024","unstructured":"Naeeni Davarani M, Arian Darestani A, Guillen Ca\u00f1as V, Azimi H, Havadaragh SH, Hashemi H, et al. Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis. Sci Rep. 2024;14(1):16304.","journal-title":"Sci Rep"},{"issue":"1","key":"1030_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01528-6","volume":"24","author":"A Rostami","year":"2024","unstructured":"Rostami A, Robatjazi M, Dareyni A, Ghorbani AR, Ganji O, Siyami M, et al. Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques. BMC Med Imaging. 2024;24(1):345.","journal-title":"BMC Med Imaging"},{"issue":"5","key":"1030_CR20","first-page":"421","volume":"13","author":"H Tavakoli","year":"2023","unstructured":"Tavakoli H, Jahromi GP, Sedaghat A. Investigating the ability of radiomics features for diagnosis of the active plaque of multiple sclerosis patients. J Biomed Phys Eng. 2023;13(5):421.","journal-title":"J Biomed Phys Eng"},{"issue":"2","key":"1030_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.compmedimag.2007.10.003","volume":"32","author":"R Khayati","year":"2008","unstructured":"Khayati R, Vafadust M, Towhidkhah F, Nabavi SM. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph. 2008;32(2):124\u201333.","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"1030_CR22","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1148\/radiol.2019191061","volume":"294","author":"PA Narayana","year":"2020","unstructured":"Narayana PA, Coronado I, Sujit SJ, Wolinsky JS, Lublin FD, Gabr RE. Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast MRI. Radiology. 2020;294(2):398\u2013404.","journal-title":"Radiology"},{"issue":"6","key":"1030_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14060632","volume":"14","author":"J Uwaeze","year":"2024","unstructured":"Uwaeze J, Narayana PA, Kamali A, Braverman V, Jacobs MA, Akhbardeh A. Automatic active lesion tracking in multiple sclerosis using unsupervised machine learning. Diagnostics (Basel). 2024;14(6):632.","journal-title":"Diagnostics (Basel)"},{"issue":"6","key":"1030_CR24","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230514","volume":"6","author":"G Brugnara","year":"2024","unstructured":"Brugnara G, Jayachandran Preetha C, Deike K, Haase R, Pinetz T, Foltyn-Dumitru M, et al. Addressing the generalizability of AI in radiology using a novel data augmentation framework with synthetic patient image data: proof-of-concept and external validation for classification tasks in multiple sclerosis. Radiol Artif Intell. 2024;6(6):e230514.","journal-title":"Radiol Artif Intell"},{"key":"1030_CR25","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.954662","volume":"16","author":"L Valencia","year":"2022","unstructured":"Valencia L, Cl\u00e8rigues A, Valverde S, Salem M, Oliver A, Rovira \u00c0, et al. Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis. Front Neurosci. 2022;16:954662.","journal-title":"Front Neurosci"},{"issue":"1","key":"1030_CR26","doi-asserted-by":"publisher","DOI":"10.1117\/1.Jmi.6.1.014005","volume":"6","author":"W Wei","year":"2019","unstructured":"Wei W, Poirion E, Bodini B, Durrleman S, Colliot O, Stankoff B, et al. Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis. J Med Imaging (Bellingham). 2019;6(1):014005. https:\/\/doi.org\/10.1117\/1.Jmi.6.1.014005.","journal-title":"J Med Imaging (Bellingham)"},{"issue":"5","key":"1030_CR27","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1097\/RLI.0000000000000640","volume":"55","author":"T Finck","year":"2020","unstructured":"Finck T, Li H, Grundl L, Eichinger P, Bussas M, M\u00fchlau M, et al. Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection. Invest Radiol. 2020;55(5):318\u201323.","journal-title":"Invest Radiol"},{"key":"1030_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.889808","volume":"16","author":"T Finck","year":"2022","unstructured":"Finck T, Li H, Schlaeger S, Grundl L, Sollmann N, Bender B, et al. Uncertainty-aware and lesion-specific image synthesis in multiple sclerosis magnetic resonance imaging: a multicentric validation study. Front Neurosci. 2022;16:889808.","journal-title":"Front Neurosci"},{"issue":"5","key":"1030_CR29","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1097\/RLI.0000000000000938","volume":"58","author":"S Schlaeger","year":"2023","unstructured":"Schlaeger S, Li HB, Baum T, Zimmer C, Moosbauer J, Byas S, et al. Longitudinal assessment of multiple sclerosis lesion load with synthetic magnetic resonance imaging\u2014a multicenter validation study. Invest Radiol. 2023;58(5):320\u20136.","journal-title":"Invest Radiol"},{"key":"1030_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.01082","volume":"10","author":"P Eisele","year":"2019","unstructured":"Eisele P, Fischer K, Szabo K, Platten M, Gass A. Characterization of contrast-enhancing and non-contrast-enhancing multiple sclerosis lesions using susceptibility-weighted imaging. Front Neurol. 2019;10:1082.","journal-title":"Front Neurol"},{"key":"1030_CR31","doi-asserted-by":"crossref","unstructured":"Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 2015;277(3):826\u201332.","DOI":"10.1148\/radiol.2015151516"},{"key":"1030_CR32","unstructured":"Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH, et al. CLAIM 2024 Update Panel.\nChecklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell 2024;6(4):e240300."}],"container-title":["Neurology and Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40120-026-01030-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40120-026-01030-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40120-026-01030-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:59:12Z","timestamp":1791305952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40120-026-01030-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,6]]},"references-count":32,"alternative-id":["1030"],"URL":"https:\/\/doi.org\/10.1007\/s40120-026-01030-x","relation":{},"ISSN":["2193-8253","2193-6536"],"issn-type":[{"value":"2193-8253","type":"print"},{"value":"2193-6536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,6]]},"assertion":[{"value":"9 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"None of the authors have any conflicts of interest to disclose.","order":1,"name":"Ethics","label":"Conflict of Interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}