ArticleScientific reports2023
Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 30 citations in OpenAlex.
- Digital twin for neurological conditions: a systematic scoping review.Biomedical engineering letters · 2026Review
- Digital twins of ex vivo human lungs enable accurate and personalized evaluation of therapeutic efficacy.Nature biotechnology · 2026Article
- BrainTwin-AI: A Multimodal MRI-EEG-Based Cognitive Digital Twin for Real-Time Brain Health Intelligence.Brain sciences · 2026Article
- Predicting multiple sclerosis from radiologically isolated syndrome using generative artificial intelligence.PLOS digital health · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Next Generation Experimental Models of Venous Physiology and Pathology: Examining Human Relevance in Recapitulating Lower Extremity Flow and Valve Function in New Approach Methods (NAMs).Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Mining the gaps: Deciphering Alzheimer's biology through AI-driven reconciliation.The journal of prevention of Alzheimer's disease · 2025Article
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 2.The journal of headache and pain · 2025Review
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
- Towards generative digital twins in biomedical research.Computational and structural biotechnology journal · 2024Review
- Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.Neurology · 2024Article
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of medical Internet research · 2024Article
- Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Evolution of digital twins in precision health applications: a scoping review study.Research square · 2024Article
- Challenges and Future Perspectives in Modeling Neurodegenerative Diseases Using Organ-on-a-Chip Technology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Digital Twins in Neuroscience.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
Corrections and comments
- Erratum issued
- Update of
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.