Goodin DS. The epidemiology of multiple sclerosis: insights to disease pathogenesis. Handb Clin Neurol. 2014;122:231–66.

Article  PubMed  Google Scholar 

Ramagopalan SV, Sadovnick AD. Epidemiology of multiple sclerosis. Neurol Clin. 2011;29:207–17.

Article  PubMed  Google Scholar 

Walton C, et al. Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS. Multiple Scler J. 2020;26:1816–21.

Article  Google Scholar 

Attfield KE, Jensen LT, Kaufmann M, Friese MA, Fugger L. The immunology of multiple sclerosis. Nat Rev Immunol. 2022;22:734–50.

Article  CAS  PubMed  Google Scholar 

Bjornevik K, et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Sci (1979). 2022;375:296–301.

CAS  Google Scholar 

Thompson AJ, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–73.

Article  PubMed  Google Scholar 

Gobbin F, et al. 2017 McDonald criteria for multiple sclerosis: earlier diagnosis with reduced specificity? Mult Scler Relat Disord. 2019;29:23–5.

Article  PubMed  Google Scholar 

Wattjes MP, et al. 2021 MAGNIMS–CMSC–NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 2021;20:653–70.

Article  PubMed  Google Scholar 

Paul F, et al. Optical coherence tomography in multiple sclerosis: a 3-year prospective multicenter study. Ann Clin Transl Neurol. 2021;8:2235–51.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rao SM, et al. Multiple sclerosis performance test: validation of self-administered neuroperformance modules. Eur J Neurol. 2020;27:878–86.

Article  CAS  PubMed  Google Scholar 

La Rosa F et al. Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues. Neuroimage Clin 103205 (2022).

Huang J, et al. Inflammation-related plasma and CSF biomarkers for multiple sclerosis. Proc Natl Acad Sci. 2020;117:12952–60.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wuschek A, et al. Somatosensory evoked potentials and magnetic resonance imaging of the central nervous system in early multiple sclerosis. J Neurol. 2023;270:824–30.

Article  PubMed  Google Scholar 

Lambe J, Saidha S, Bermel RA. Optical coherence tomography and multiple sclerosis: update on clinical application and role in clinical trials. Multiple Scler J. 2020;26:624–39.

Article  Google Scholar 

Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388:1201–8.

Article  CAS  PubMed  Google Scholar 

Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med. 2020;3:126.

Article  PubMed  PubMed Central  Google Scholar 

AI vs Machine Learning. How Do They Differ? Googlehttps://cloud.google.com/learn/artificial-intelligence-vs-machine-learning.

Google. What is Machine Learning? https://developers.google.com/machine-learning/intro-to-ml/what-is-ml.

Auger SD, Jacobs BM, Dobson R, Marshall CR, Noyce AJ. Big data, machine learning and artificial intelligence: a neurologist’s guide. Pract Neurol (2020).

Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Front Med (Lausanne). 2020;7:27.

Article  PubMed  Google Scholar 

Afzal HMR, Luo S, Ramadan S, Lechner-Scott J. The emerging role of artificial intelligence in multiple sclerosis imaging. Multiple Scler J. 2022;28:849–58.

Article  Google Scholar 

Jones DT, Kerber KA. Artificial intelligence and the practice of neurology in 2035: the neurology future forecasting series. Neurology. 2022;98:238–45.

Article  PubMed  Google Scholar 

Patel UK, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268:1623–42.

Article  PubMed  Google Scholar 

Soun JE, et al. Artificial intelligence and acute stroke imaging. Am J Neuroradiol. 2021;42:2–11.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging applications of artificial intelligence in neuro-oncology. Radiology. 2019;290:607–18.

Article  PubMed  Google Scholar 

Calabrese E, Villanueva-Meyer JE, Cha S. A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Sci Rep. 2020;10:11852.

Article  CAS  PubMed  PubMed Central  Google Scholar 

An S, Kang C, Lee HW. Artificial intelligence and computational approaches for epilepsy. J Epilepsy Res. 2020;10:8.

Article  PubMed  PubMed Central  Google Scholar 

Sibley KG, Girges C, Hoque E, Foltynie T. Video-based analyses of Parkinson’s disease severity: a brief review. J Parkinsons Dis. 2021;11:S83–93.

Article  PubMed  PubMed Central  Google Scholar 

Mezzaroba L, et al. Antioxidant and anti-inflammatory diagnostic biomarkers in multiple sclerosis: a machine learning study. Mol Neurobiol. 2020;57:2167–78.

Article  CAS  PubMed  Google Scholar 

Seitz CB, et al. Serum neurofilament levels reflect outer retinal layer changes in multiple sclerosis. Ther Adv Neurol Disord. 2021;14:17562864211003478.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Brummer T, et al. Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers. Brain Commun. 2022;4:fcac153.

Article  PubMed  PubMed Central  Google Scholar 

Gaetani L, et al. The Immune signature of CSF in multiple sclerosis with and without Oligoclonal bands: a Machine Learning Approach to Proximity Extension Assay Analysis. Int J Mol Sci. 2023;25:139.

Article  PubMed  PubMed Central  Google Scholar 

Martynova E et al. Serum and cerebrospinal fluid cytokine biomarkers for diagnosis of multiple sclerosis. Mediators Inflamm 2020, (2020).

Lopez-Soley E, et al. Diffusion tensor imaging metrics associated with future disability in multiple sclerosis. Sci Rep. 2023;13:3565.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Eshaghi A et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 12, 2078 (2021).).** The authors used unsupervised machine learning over the brain scans of 6322 MS patients to define new MS subtypes based on MRI data only. This led to three MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led that better define disability progression and response to treatment.

Zhang L, Dai H, Sang Y. Med-SRNet: GAN-based medical image super-resolution via high-resolution representation learning. Comput Intell Neurosci 2022, (2022).

Bouman PM, et al. Artificial double inversion recovery images for (juxta) cortical lesion visualization in multiple sclerosis. Multiple Scler J. 2022;28:541–9.

Article  CAS  Google Scholar 

Alexander DC, et al. Image quality transfer and applications in diffusion MRI. NeuroImage. 2017;152:283–98.

Article  PubMed  Google Scholar 

Cerri S, et al. A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. NeuroImage. 2021;225:117471.

Article  PubMed  Google Scholar 

Billot B, et al. SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal. 2023;86:102789. In this paper the authors overcome the AI problem of the domain adaptation fully randomising the generation of images of multiple contrast and resolution using generative models. This generative AI approach, applied to MRI brain segmentation, exhibits an excellent generalisation compared to other AI solutions.

Article  PubMed