Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
Highlights
The technology of digital twins (DTs) is a promising concept that has become the focus of interest in industry and, in recent years, in healthcare sector as well
Zhao et al found that support vector machines incorporating short-term clinical and brain magnetic resonance imaging (MRI) data were better at predicting disease progression of multiple sclerosis (MS) and selecting patients for more aggressive treatments than logistic regression methods [207]
Before DTs can be used in patient care, they must be validated by studies and experts, as well as by real-world investigations to show the effectiveness and safety of their methods
Summary
The technology of digital twins (DTs) is a promising concept that has become the focus of interest in industry and, in recent years, in healthcare sector as well. DTs are a revolutionary tool for an improved characterization and prediction of disease course and for deep clinical phenotyping of pwMS [118] In this regard, big data analysis via ML supports visualization of the DTMS at different stages of MS and enables further therapeutic decisions. As a biophysical technique for measuring magnetic properties and generating weighted images of relative tissue contrasts, MRI offers both volumetric and dynamic quantitative means of detecting pathological tissue changes These represent a promising approach to optimizing MS management through in vivo monitoring in the assessment of the course of chronic diseases by recording their disease-related dynamics or treatment-induced effects [146, 147]. This describes the scope of the instruments, which must later be applied to individual patients in practice in order to derive a comparable trajectories
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