A future of AI-driven personalized care for people with multiple sclerosis
Language English Country Switzerland Media electronic-ecollection
Document type Journal Article
PubMed
39224590
PubMed Central
PMC11366570
DOI
10.3389/fimmu.2024.1446748
Knihovny.cz E-resources
- Keywords
- AI, data, diagnosis, disease progression, multiple sclerosis, personalized medicine, prognosis,
- MeSH
- Biomarkers MeSH
- Precision Medicine * methods trends MeSH
- Quality of Life MeSH
- Humans MeSH
- Prognosis MeSH
- Disease Progression MeSH
- Multiple Sclerosis * therapy immunology MeSH
- Artificial Intelligence * trends MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Biomarkers MeSH
Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.
AB Science Clinical Development Paris France
Bristol Myers Squibb Company Corp Princeton NJ United States
Department of Computer Science Aalto University Espoo Finland
Department of Neurology Vita Salute San Raffaele University Ospedale San Raffaele Milan Italy
Department of Neurorehabilitative Sciences Casa di Cura Igea Italy
Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland
European Charcot Foundation Brussels Belgium
F Hoffmann La Roche Ltd Product Development Medical Affairs Neuroscience Basel Switzerland
Institute of Neuroradiology St Josef Hospital Ruhr University Bochum Bochum Germany
Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany
SYNAPSE Research Management Partners Madrid Spain
Univ Lille InsermU1172 LilNCog CHU Lille FHU Precise Lille France
See more in PubMed
Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. (2018) 17:162–73. doi: 10.1016/s1474-4422(17)30470-2 PubMed DOI
Tur C, Carbonell-Mirabent P, Cobo-Calvo A, Otero-Romero S, Arrambide G, Midaglia L, et al. . Association of early progression independent of relapse activity with long-term disability after a first demyelinating event in multiple sclerosis. JAMA Neurol. (2023) 80:151. doi: 10.1001/jamaneurol.2022.4655 PubMed DOI PMC
Kobelt G, Berg J, Lindgren P, Jönsson B. Costs and quality of life in multiple sclerosis in Europe: method of assessment and analysis. Eur J Health Econ. (2006) 7:5–13. doi: 10.1007/s10198-006-0365-y PubMed DOI
Tutuncu M, Tang J, Zeid NA, Kale N, Crusan DJ, Atkinson EJ, et al. . Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Multiple Sclerosis. (2013) 19:188–98. doi: 10.1177/1352458512451510 PubMed DOI PMC
Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ, et al. . Defining the clinical course of multiple sclerosis. Neurology. (2014) 83:278–86. doi: 10.1212/wnl.0000000000000560 PubMed DOI PMC
Kuhlmann T, Moccia M, Coetzee T, Cohen JA, Correale J, Graves J, et al. . Multiple sclerosis progression: time for a new mechanism-driven framework. Lancet Neurol. (2023) 22:78–88. doi: 10.1016/s1474-4422(22)00289-7 PubMed DOI PMC
Marrie RA, Horwitz RI, Cutter G, Tyry T, Vollmer T. Association between comorbidity and clinical characteristics of MS. Acta Neurol Scand. (2011) 124:135–41. doi: 10.1111/j.1600-0404.2010.01436.x PubMed DOI PMC
Marrie RA, Cohen JA, Stuve O, Trojano M, Sørensen PS, Reingold S, et al. . A systematic review of the incidence and prevalence of comorbidity in multiple sclerosis: Overview. Multiple Sclerosis. (2015) 21:263–81. doi: 10.1177/1352458514564491 PubMed DOI PMC
Lublin FD, Häring DA, Ganjgahi H, Ocampo A, Hatami F, Čuklina E, et al. . How patients with multiple sclerosis acquire disability. Brain. (2022) 145:3147–61. doi: 10.1093/brain/awac016 PubMed DOI PMC
Kappos L, Wolinsky JS, Giovannoni G, Arnold DL, Wang Q, Bernasconi C, et al. . Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. (2020) 77:1132. doi: 10.1001/jamaneurol.2020.1568 PubMed DOI PMC
Portaccio E, Bellinvia A, Fonderico M, Pastò L, Razzolini L, Totaro R, et al. . Progression is independent of relapse activity in early multiple sclerosis: a real-life cohort study. Brain. (2022) 145:2796–805. doi: 10.1093/brain/awac111 PubMed DOI
Lassmann H. Targets of therapy in progressive MS. Multiple Sclerosis. (2017) 23:1593–9. doi: 10.1177/1352458517729455 PubMed DOI
Lassmann H. Pathogenic mechanisms associated with different clinical courses of multiple sclerosis. Front Immunol. (2019) 9:3116. doi: 10.3389/fimmu.2018.03116 PubMed DOI PMC
Cagol A, Schaedelin S, Barakovic M, Benkert P, Todea RA, Rahmanzadeh R, et al. . Association of brain atrophy with disease progression independent of relapse activity in patients with relapsing multiple sclerosis. JAMA Neurol. (2022) 79:682. doi: 10.1001/jamaneurol.2022.1025 PubMed DOI PMC
Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, et al. . Spinal cord atrophy predicts progressive disease in relapsing multiple sclerosis. Ann Neurol. (2022) 91:268–81. doi: 10.1002/ana.26281 PubMed DOI PMC
Cagol A, Benkert P, Melie-Garcia L, Schaedelin SA, Leber S, Tsagkas C, et al. . Association of spinal cord atrophy and brain paramagnetic rim lesions with progression independent of relapse activity in people with MS. Neurology. (2024) 102:e207768. doi: 10.1212/wnl.0000000000207768 PubMed DOI PMC
UCA San Francisco MS-EPIC Team. Bruce ACC, Hollenbach JA, Bove R, Kirkish G, Sacco S, et al. . Silent progression in disease activity–free relapsing multiple sclerosis. Ann Neurol. (2019) 85:653–66. doi: 10.1002/ana.25463 PubMed DOI PMC
Krieger SC, Cook K, De Nino S, Fletcher M. The topographical model of multiple sclerosis. Neurology® Neuroimmunol Neuroinflamm. (2016) 3:e279. doi: 10.1212/nxi.0000000000000279 PubMed DOI PMC
Krieger SC, Billiet T, Maes C, Barros N, Ribbens A, Wang C, et al. . MSMilan2023 – paper poster - session 1' (2023). Multiple Sclerosis. (2023) 29:137–393. doi: 10.1177/13524585231196192 DOI
Krieger SC, Antoine A, Sumowski JF. EDSS 0 is not normal: Multiple sclerosis disease burden below the clinical threshold. Multiple Sclerosis. (2022) 28:2299–303. doi: 10.1177/13524585221108297 PubMed DOI
Runia TF, Jafari N, Siepman DAM, Hintzen RQ. Fatigue at time of CIS is an independent predictor of a subsequent diagnosis of multiple sclerosis. J Neurol Neurosurg Psychiatry. (2015) 86:543–6. doi: 10.1136/jnnp-2014-308374 PubMed DOI
Paul F. Pathology and MRI: exploring cognitive impairment in MS. Acta Neurol Scand. (2016) 134:24–33. doi: 10.1111/ane.12649 PubMed DOI
Kalincik T, Manouchehrinia A, Sobisek L, Jokubaitis V, Spelman T, Horakova D, et al. . Towards personalized therapy for multiple sclerosis: prediction of individual treatment response. Brain. (2017) 140:2426–43. doi: 10.1093/brain/awx185 PubMed DOI
Montalban X, Gold R, Thompson AJ, Otero-Romero S, Amato MP, Chandraratna D, et al. . ECTRIMS/EAN Guideline on the pharmacological treatment of people with multiple sclerosis. Multiple Sclerosis J. (2018) 24:96–120. doi: 10.1177/1352458517751049 PubMed DOI
Wingerchuk DM, Carter JL. Multiple sclerosis: current and emerging disease-modifying therapies and treatment strategies. Mayo Clin Proc. (2014) 89:225–40. doi: 10.1016/j.mayocp.2013.11.002 PubMed DOI
Gasperini C, Prosperini L, Tintoré M, Sormani MP, Filippi M, Rio J, et al. . Unraveling treatment response in multiple sclerosis. Neurology. (2019) 92:180–92. doi: 10.1212/wnl.0000000000006810 PubMed DOI PMC
Amin M, Hersh CM. Updates and advances in multiple sclerosis neurotherapeutics. Neurodegenerative Dis Manage. (2023) 13:47–70. doi: 10.2217/nmt-2021-0058 PubMed DOI PMC
Bayas A, Christ M, Faissner S, Klehmet J, Pul R, Skripuletz T, et al. . Disease-modifying therapies for relapsing/active secondary progressive multiple sclerosis – a review of population-specific evidence from randomized clinical trials. Ther Adv Neurol Disord. (2023) 16:175628642211468. doi: 10.1177/17562864221146836 PubMed DOI PMC
Visser LH, Van Der Zande A. Reasons patients give to use or not to use immunomodulating agents for multiple sclerosis. Eur J Neurol. (2011) 18:1343–9. doi: 10.1111/j.1468-1331.2011.03411.x PubMed DOI
Jokubaitis VG, Spelman T, Lechner-Scott J, Barnett M, Shaw C, Vucic S, et al. . The Australian Multiple Sclerosis (MS) Immunotherapy Study: A prospective, multicentre study of drug utilisation using the MSBase platform. PloS One. (2013) 8:e59694. doi: 10.1371/journal.pone.0059694 PubMed DOI PMC
Saposnik G, Andhavarapu S, de la Maza SS, Castillo-Triviño T, Borges M, Barón BP, et al. . Delayed cognitive processing and treatment status quo bias in early-stage multiple sclerosis. Multiple Sclerosis Related Disord. (2022) 68:104138. doi: 10.1016/j.msard.2022.104138 PubMed DOI
Giovannoni G, Butzkueven H, Dhib-Jalbut S, Hobart J, Kobelt G, Pepper G, et al. . Brain health: time matters in multiple sclerosis. Multiple Sclerosis Related Disord. (2016) 9:S5–S48. doi: 10.1016/j.msard.2016.07.003 PubMed DOI
Inojosa H, Proschmann U, Akgün K, Ziemssen T. The need for a strategic therapeutic approach: multiple sclerosis in check. Ther Adv Chronic Dis. (2022) 13:204062232110630. doi: 10.1177/20406223211063032 PubMed DOI PMC
Harding K, Williams O, Willis M, Hrastelj J, Rimmer A, Joseph F, et al. . Clinical outcomes of escalation vs early intensive disease-modifying therapy in patients with multiple sclerosis. JAMA Neurol. (2019) 76:536. doi: 10.1001/jamaneurol.2018.4905 PubMed DOI PMC
Buron MD, Chalmer TA, Sellebjerg F, Barzinji I, Bech D, Christensen JR, et al. . Initial high-efficacy disease-modifying therapy in multiple sclerosis. Neurology. (2020) 95:e1041–51. doi: 10.1212/wnl.0000000000010135 PubMed DOI
Simpson A, Mowry EM, Newsome SD. Early aggressive treatment approaches for multiple sclerosis. Curr Treat Options Neurol. (2021) 23:19. doi: 10.1007/s11940-021-00677-1 PubMed DOI PMC
Spelman T, Magyari M, Piehl F, Svenningsson A, Rasmussen PV, Kant M, et al. . Treatment escalation vs immediate initiation of highly effective treatment for patients with relapsing-remitting multiple sclerosis. JAMA Neurol. (2021) 78:1197. doi: 10.1001/jamaneurol.2021.2738 PubMed DOI PMC
Freeman L, Longbrake EE, Coyle PK, Hendin B, Vollmer T. High-efficacy therapies for treatment-naïve individuals with relapsing–remitting multiple sclerosis. CNS Drugs. (2022) 36:1285–99. doi: 10.1007/s40263-022-00965-7 PubMed DOI PMC
Weideman AM, Tapia-Maltos MA, Johnson K, Greenwood M, Bielekova B. Meta-analysis of the age-dependent efficacy of multiple sclerosis treatments. Front Neurol. (2017) 8:577. doi: 10.3389/fneur.2017.00577 PubMed DOI PMC
He A, Merkel B, Brown JWL, Ryerson LZ, Kister I, Malpas CB, et al. . Timing of high-efficacy therapy for multiple sclerosis: a retrospective observational cohort study. Lancet Neurol. (2020) 19:307–16. doi: 10.1016/s1474-4422(20)30067-3 PubMed DOI
Krämer J, Bar-Or A, Turner TJ, Wiendl H. Bruton tyrosine kinase inhibitors for multiple sclerosis. Nat Rev Neurol. (2023) 19:289–304. doi: 10.1038/s41582-023-00800-7 PubMed DOI PMC
Hult KJ. Measuring the potential health impact of personalized medicine: evidence from multiple sclerosis treatments. In: Economic Dimensions of Personalized and Precision Medicine (2019) (Chicago, USA: University of Chicago Press; ). p. 185–216. doi: 10.7208/chicago/9780226611235.003.0008 DOI
Van Wijmeersch B, Hartung HP, Vermersch P, Pugliatti M, Pozzilli C, Grigoriadis N, et al. . Using personalized prognosis in the treatment of relapsing multiple sclerosis: A practical guide. Front Immunol. (2022) 13:991291. doi: 10.3389/fimmu.2022.991291 PubMed DOI PMC
Marrie RA, Fisk JD, Fitzgerald K, Kowalec K, Maxwell C, Rotstein D, et al. . Etiology, effects and management of comorbidities in multiple sclerosis: recent advances. Front Immunol. (2023) 14:1197195. doi: 10.3389/fimmu.2023.1197195 PubMed DOI PMC
Yang J, Hamade M, Wu Q, Wang Q, Axtell R, Giri S, et al. . Current and future biomarkers in multiple sclerosis. Int J Mol Sci. (2022) 23:5877. doi: 10.3390/ijms23115877 PubMed DOI PMC
Voigt I, Inojosa H, Wenk J, Akgün K, Ziemssen T. Building a monitoring matrix for the management of multiple sclerosis. Autoimmun Rev. (2023) 22:103358. doi: 10.1016/j.autrev.2023.103358 PubMed DOI
Gill AJ, Schorr EM, Gadani SP, Calabresi PA. Emerging imaging and liquid biomarkers in multiple sclerosis. Eur J Immunol. (2023) 53:2250228. doi: 10.1002/eji.202250228 PubMed DOI PMC
Trentzsch K, Schumann P, Śliwiński G, Bartscht P, Haase R, Schriefer D, et al. . Using machine learning algorithms for identifying gait parameters suitable to evaluate subtle changes in gait in people with multiple sclerosis. Brain Sci. (2021) 11:1049. doi: 10.3390/brainsci11081049 PubMed DOI PMC
Guerrieri S, Comi G, Leocani L. Optical coherence tomography and visual evoked potentials as prognostic and monitoring tools in progressive multiple sclerosis. Front Neurosci. (2021) 15:692599. doi: 10.3389/fnins.2021.692599 PubMed DOI PMC
Paul F, Calabresi PA, Barkhof F, Green AJ, Kardon R, Sastre-Garriga J, et al. . Optical coherence tomography in multiple sclerosis: A 3-year prospective multicenter study. Ann Clin Trans Neurol. (2021) 8:2235–51. doi: 10.1002/acn3.51473 PubMed DOI PMC
Graves JS. Identifying multiple sclerosis activity. Neurology. (2022) 99:269–70. doi: 10.1212/wnl.0000000000200903 PubMed DOI
Absinta M, Sati P, Masuzzo F, Nair G, Sethi V, Kolb H, et al. . Association of chronic active multiple sclerosis lesions with disability in vivo . JAMA Neurol. (2019) 76:1474. doi: 10.1001/jamaneurol.2019.2399 PubMed DOI PMC
Blindenbacher N, Brunner E, Asseyer S, Scheel M, Siebert N, Rasche L, et al. . Evaluation of the ‘ring sign’ and the ‘core sign’ as a magnetic resonance imaging marker of disease activity and progression in clinically isolated syndrome and early multiple sclerosis. Multiple Sclerosis J - Exp Trans Clin. (2020) 6:205521732091548. doi: 10.1177/2055217320915480 PubMed DOI PMC
Preziosa P, Pagani E, Meani A, Moiola L, Rodegher M, Filippi M, et al. . Slowly expanding lesions predict 9-Year multiple sclerosis disease progression. Neurology® Neuroimmunol Neuroinflamm. (2022) 9:e1139. doi: 10.1212/nxi.0000000000001139 PubMed DOI PMC
Oreja-Guevara C, Blanco TA, Ruiz LB, Pérez MAH, Meca-Lallana V, Ramió-Torrentà L. Cognitive dysfunctions and assessments in multiple sclerosis. Front Neurol. (2019) 10:581. doi: 10.3389/fneur.2019.00581 PubMed DOI PMC
Podda J, Ponzio M, Pedullà L, Bragadin MM, Battaglia MA, Zaratin P, et al. . Predominant cognitive phenotypes in multiple sclerosis: Insights from patient-centered outcomes. Multiple Sclerosis Related Disord. (2021) 51:102919. doi: 10.1016/j.msard.2021.102919 PubMed DOI
Carotenuto A, Costabile T, Pontillo G, Moccia M, Falco F, Petracca M, et al. . Cognitive trajectories in multiple sclerosis: a long-term follow-up study. Neurol Sci. (2021) 43:1215–22. doi: 10.1007/s10072-021-05356-2 PubMed DOI PMC
Brichetto G, Zaratin P. Measuring outcomes that matter most to people with multiple sclerosis: the role of patient-reported outcomes. ' Curr Opin Neurol. (2020) 33:295–9. doi: 10.1097/wco.0000000000000821 PubMed DOI PMC
Zaratin P, Vermersch P, Amato MP, Brichetto G, Coetzee T, Cutter G, et al. . The agenda of the global patient reported outcomes for multiple sclerosis (PROMS) initiative: Progresses and open questions. Multiple Sclerosis Related Disord. (2022) 61:103757. doi: 10.1016/j.msard.2022.103757 PubMed DOI
Peeters LM, Parciak T, Kalra D, Moreau Y, Kasilingam E, van Galen P, et al. . Multiple Sclerosis Data Alliance – A global multi-stakeholder collaboration to scale-up real world data research. Multiple Sclerosis Related Disord. (2021) 47:102634. doi: 10.1016/j.msard.2020.102634 PubMed DOI
Yamout B, Sahraian M, Bohlega S, Al-Jumah M, Goueider R, Dahdaleh M, et al. . Consensus recommendations for the diagnosis and treatment of multiple sclerosis: 2019 revisions to the MENACTRIMS guidelines. Multiple Sclerosis Related Disord. (2020) 37:101459. doi: 10.1016/j.msard.2019.101459 PubMed DOI
Wattjes MP, Ciccarelli O, Reich DS, Banwell B, de Stefano N, Enzinger C, et al. . 2021 MAGNIMS–CMSC–NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. (2021) 20:653–70. doi: 10.1016/s1474-4422(21)00095-8 PubMed DOI
Tur C, Moccia M, Barkhof F, Chataway J, Sastre-Garriga J, Thompson AJ, et al. . Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting. Nat Rev Neurol. (2018) 14:75–93. doi: 10.1038/nrneurol.2017.171 PubMed DOI
Voigt I, Benedict M, Susky M, Scheplitz T, Frankowitz S, Kern R, et al. . A digital patient portal for patients with multiple sclerosis. Front Neurol. (2020) 11:400. doi: 10.3389/fneur.2020.00400 PubMed DOI PMC
Wenk J, Voigt I, Inojosa H, Schlieter H, Ziemssen T. Building digital patient pathways for the management and treatment of multiple sclerosis. Front Immunol. (2024) 15:1356436. doi: 10.3389/fimmu.2024.1356436 PubMed DOI PMC
Sima DM, Esposito G, Van Hecke W, Ribbens A, Nagels G, Smeets D. Health economic impact of software-assisted brain MRI on therapeutic decision-making and outcomes of relapsing-remitting multiple sclerosis patients—A microsimulation study. Brain Sci. (2021) 11:1570. doi: 10.3390/brainsci11121570 PubMed DOI PMC
Parciak T, Geys L, Helme A, van der Mei I, Hillert J, Schmidt H, et al. . Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force. Multiple Sclerosis. (2023) 30:396–418. doi: 10.1177/13524585231216004 PubMed DOI PMC
Sastre-Garriga J, Pareto D, Battaglini M, Rocca MA, Ciccarelli O, Enzinger C, et al. . MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol. (2020) 16:171–82. doi: 10.1038/s41582-020-0314-x PubMed DOI PMC
Aboseif A, Roos I, Krieger SC, Kalincik T, Hersh CM. Leveraging Real-World evidence and observational studies in treating multiple sclerosis. Neurol Clinics. (2024) 42:203–27. doi: 10.1016/j.ncl.2023.06.003 PubMed DOI
Katkade VB, Sanders KN, Zou KH. Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J Multidiscip Healthcare. (2018) 11:295–304. doi: 10.2147/jmdh.s160029 PubMed DOI PMC
Vercruyssen S, Brys A, Verheijen M, Steach B, Van Vlierberge E, Sima DM, et al. . Abstracts from the 34th annual meeting of the consortium of multiple sclerosis centers. Int J MS Care. (2020) 22:1–116. doi: 10.7224/1537-2073-22.s2.1 PubMed DOI
Yadav SK, Motamedi S, Oberwahrenbrock T, Oertel FC, Polthier K, Paul F, et al. . CuBe: parametric modeling of 3D foveal shape using cubic Bézier. Biomed Optics Express. (2017) 8:4181. doi: 10.1364/boe.8.004181 PubMed DOI PMC
Yadav SK, Kadas EM. Optic nerve head three-dimensional shape analysis. J Biomed Optics. (2018) 23:1. doi: 10.1117/1.jbo.23.10.106004 PubMed DOI
Rakić M, Vercruyssen S, Van Eyndhoven S, de la Rosa E, Jain S, Van Huffel S, et al. . 'icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions. NeuroImage Clin. (2021) 31:102707. doi: 10.1016/j.nicl.2021.102707 PubMed DOI PMC
Van Hecke W, Costers L, Descamps A, Ribbens A, Nagels G, Smeets D, et al. . A novel digital care management platform to monitor clinical and subclinical disease activity in multiple sclerosis. Brain Sci. (2021) 11:1171. doi: 10.3390/brainsci11091171 PubMed DOI PMC
Dillenseger A, Weidemann ML, Trentzsch K, Inojosa H, Haase R, Schriefer D, et al. . Digital biomarkers in multiple sclerosis. Brain Sci. (2021) 11:1519. doi: 10.3390/brainsci11111519 PubMed DOI PMC
Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital twins for multiple sclerosis. Front Immunol. (2021) 12:669811. doi: 10.3389/fimmu.2021.669811 PubMed DOI PMC
Scholz M, Haase R, Trentzsch K, Stölzer-Hutsch H, Ziemssen T. Improving digital patient care: lessons learned from patient-reported and expert-reported experience measures for the clinical practice of multidimensional walking assessment. Brain Sci. (2021) 11:786. doi: 10.3390/brainsci11060786 PubMed DOI PMC