Abstract:Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary. At advanced stages of MS with significant walking impairment, this assessment is also necessary to optimize symptomatic treatment, choose the m… Show more
“…Subjects were tested with GAITRite (CIR-Systems Inc., Franklin, NJ, USA) according to the Dresden Protocol for Multidimensional Gait Assessment (DMWA) [33]. The GAITRite was investigated in numerous studies and has demonstrated high reliability and validity [34][35][36].…”
Section: Methodsmentioning
confidence: 99%
“…Selected gait parameters for people with MS and healthy controls (N = 60). Key parameters used in clinical routine according our DMWA protocol[33]; MS = multiple sclerosis; HC = healthy controls; EMIQ = Early Mobility Impairment Questionnaire; MSWS = Multiple Sclerosis Walking Scale; GCT = Gait Cycle Time; L = left; R = right; standard deviation = SD; data in mean ± SD; ( )-dimensionless values; p-value via Mann-Whitney U-Test for differences between groups.…”
In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.
“…Subjects were tested with GAITRite (CIR-Systems Inc., Franklin, NJ, USA) according to the Dresden Protocol for Multidimensional Gait Assessment (DMWA) [33]. The GAITRite was investigated in numerous studies and has demonstrated high reliability and validity [34][35][36].…”
Section: Methodsmentioning
confidence: 99%
“…Selected gait parameters for people with MS and healthy controls (N = 60). Key parameters used in clinical routine according our DMWA protocol[33]; MS = multiple sclerosis; HC = healthy controls; EMIQ = Early Mobility Impairment Questionnaire; MSWS = Multiple Sclerosis Walking Scale; GCT = Gait Cycle Time; L = left; R = right; standard deviation = SD; data in mean ± SD; ( )-dimensionless values; p-value via Mann-Whitney U-Test for differences between groups.…”
In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.
“…In recent years, the Expanded Disease Disability Scale (EDSS) has been an essential, irreplaceable scale in MS which has been improved in the past years by different approaches (127)(128)(129). However, other additional clinical instruments have been introduced to quantitate the different multidimensional aspects of MS as fatigue, cognition or walking function (130,131). The Multiple Sclerosis Functional Composite (MSFC) provides a functional assessment of different key functions (upper and lower extremities, cognition) that is used more and more frequently in MS and has been proven to be highly sensitive in the evaluation of very important clinical trials.…”
Section: Patients Physiological Status Datamentioning
confidence: 99%
“…The Multiple Sclerosis Performance Test (MSPT) is a digital adaptation of the MSFC with additional elements added ( 160 , 161 ) and measures health status via iPad with questionnaire on health status, processing speed with Processing Speed Test (PST) ( 162 ), manual skills with 9-Hole-Peg-Test (9-HPT) and walking speed with Timed 25-Foot-Walk (T25-FW) ( 160 ). Multidimensional gait analysis can be performed with measurement of walking speed (T25-FW), measurement of endurance [2-Minute Walk Test, 2MWT ( 163 , 164 )] and measurement of balance and gait quality on a sensor-based walking mat (GAITRite ® -System, Mobility Lab-System) ( 131 ). For the digital measurement of data in patient-specific everyday life (at home) there are various patient apps such as Floodlight, diverse fitness tracker and health apps available ( 165 , 166 ).…”
Section: Concept Of Digital Twins In the Management Of Multiple Sclerosismentioning
confidence: 99%
“…Thanks to the visualized simulation of the DT, the HCP has time to address all patients’ questions and concerns in detail. Examples of existing dashboards for displaying individual patient data at a glance include the walking assessment dashboard as part of the multidimensional digital patient management system MSDS 3D ( 201 , 202 ), showing the results of clinical multidimensional walking assessment and daily smart monitoring longitudinally ( 131 ), and the MS BioScreen, that integrates multiple dimensions of disease information: clinical evolution, therapeutic interventions, brain, eye, and spinal cord imaging, environmental exposures, genomics, and biomarker data ( 56 , 203 ).…”
Section: Use Cases In Care Of Multiple Sclerosismentioning
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.