⚡ Quick Summary
This study utilized AI-assisted analysis of over 13,000 assessments from individuals with secondary progressive multiple sclerosis (MS) to identify distinct disability patterns within identical Expanded Disability Status Scale (EDSS) scores. The findings revealed the potential to differentiate into four unique subscore patterns for patients with EDSS scores of 4.0 or higher.
🔍 Key Details
- 📊 Dataset: 13,103 assessments from 1,636 individuals with secondary progressive MS
- 🧩 Features used: Functional System scores (FSS), Ambulation scores, and EDSS scores
- ⚙️ Technology: Machine learning algorithms for clustering
- 🏆 Key finding: Identification of four distinct subscore patterns within identical EDSS scores
🔑 Key Takeaways
- 📊 EDSS scores are commonly used to measure disability in MS but may not capture all relevant disability patterns.
- 💡 AI technology can enhance the understanding of disability by analyzing large clinical datasets.
- 👩🔬 The study analyzed data from the EXPAND trial, focusing on secondary progressive MS.
- 🏆 Clustering algorithms successfully identified multiple disability patterns within the same EDSS score.
- 🤖 This research highlights the importance of considering functional domains beyond ambulation in disability assessments.
- 🌍 The findings could lead to more personalized treatment approaches for MS patients.
- 🔍 Future research may explore the application of these findings in clinical practice.
📚 Background
The Expanded Disability Status Scale (EDSS) is a widely recognized tool for assessing disability in individuals with multiple sclerosis (MS). However, it has been noted that scores of 4.5 and above primarily focus on ambulation, potentially overlooking other critical aspects of disability. Understanding the full spectrum of disability in MS is essential for improving patient care and treatment outcomes.
🗒️ Study
This study analyzed a comprehensive dataset from the EXPAND trial, which included 13,103 assessments from 1,636 individuals diagnosed with secondary progressive MS. The researchers aimed to explore how different assessments categorized under the same EDSS score could reveal distinct disability patterns. By employing machine learning algorithms, they sought to cluster the data and identify new subscore patterns based on dominant features.
📈 Results
The application of clustering algorithms resulted in the identification of several clusters, effectively grouping assessments with similar patterns. Notably, for patients with EDSS scores of 4.0 or higher, the study successfully differentiated into four unique subscore patterns. This finding underscores the potential of AI to enhance our understanding of disability in MS.
🌍 Impact and Implications
The implications of this study are significant for the field of neurology and MS treatment. By leveraging AI technology to analyze large datasets, healthcare professionals can gain deeper insights into the varying disability patterns among patients with identical EDSS scores. This could lead to more tailored treatment strategies, ultimately improving the quality of life for individuals living with MS.
🔮 Conclusion
This research highlights the transformative potential of AI-assisted analysis in understanding disability patterns in multiple sclerosis. By identifying distinct subscore patterns within identical EDSS scores, we can pave the way for more personalized and effective treatment approaches. The future of MS care looks promising as we continue to explore the integration of advanced technologies in clinical assessments.
💬 Your comments
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AI-assisted identification of disability patterns within identical EDSS grades.
Abstract
BACKGROUND: The Neurostatus-Expanded Disability Status Scale (EDSS) is the most frequently used measure of disability in multiple sclerosis (MS) trials. However, EDSS scores ⩾4.5 are mainly based on ambulation and may fail to capture relevant disability patterns in other functional domains.
OBJECTIVE: The objective was to determine how assessments categorized with the same EDSS score may reflect distinct disability patterns.
METHODS: We analysed 13,103 assessments from 1636 people with secondary progressive MS, from the EXPAND trial. The data set is composed of Functional System scores (FSS) and their corresponding subscores, Ambulation scores and EDSS scores. We performed a descriptive analysis to define the relevant Functional Systems (FS). The subscores were then binarized based on the Neurostatus definition and grouped by respective EDSS scores. Finally, we applied two consecutive machine learning algorithms, to cluster the data. New subscore patterns were then created by aggregating clusters based on their dominant features.
RESULTS: The clustering algorithm yielded numerous clusters, grouping assessments with similar patterns. In patients with EDSS ⩾4.0, our approach allowed differentiation into four subscore patterns within the same EDSS score.
CONCLUSION: Applying Artificial Intelligence (AI) to large data sets of high-quality clinical assessments allows for distinguishing among different subscore patterns within identical EDSS scores.
Author: [‘Greselin M’, ‘Lu PJ’, ‘Mroczek M’, ‘Cerdá-Fuertes N’, ‘Demirtzoglou A’, ‘Papadopoulou A’, ‘Kuhle J’, ‘Leppert D’, ‘Arnould S’, ‘Aoun M’, ‘Kappos L’, ‘Granziera C’, “D’Souza M”]
Journal: Mult Scler
Citation: Greselin M, et al. AI-assisted identification of disability patterns within identical EDSS grades. AI-assisted identification of disability patterns within identical EDSS grades. 2025; (unknown volume):13524585251327300. doi: 10.1177/13524585251327300