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Big Data in Multiple Sclerosis
- 1. E. Convolutional Neural Networks (CNN) FLAIR Fully connected layers Output Cortical lesion98% Periventricular lesion75% Infratentorial lesion10% Spinal lesion2% Input Convolution + pooling layers B. Additional supporting data Multiple Sclerosis patients Robles® C. Precision medicine D. Personalized medicine Internet •Social media •Web-base applications •Expert Pacient Programme •Patient empowerment Administrative •Healthcare delivery •Computer support e-Records •Electronic medical records •Primary care records •Registries (medical processes) •National and international registries •Self-reported patient surveys •Personal databases A. Healthcare-relevant Big Data in MS Clinical •Disability •Cognitive impairment •Relapses •Symptoms related •Age at onset? •Gender? Radiological •Number of lesions •Spinal lesions •Cortical/yuxtacortical lesions •Gadolinium enhancement •Atrophy Environmental •Epstein Barr virus •Cigarrete smoking •Vitamin D •Sun exposure •Latitude gradient •Microbiote? Laboratory •Oligoclonal bands (IgG/IgM) •Chitinase 3-Like 1 •Light chain neurofilaments Genetic •HLA-DRB1 •Non-HLA variants (≈ 200) Epigenetic •Micro RNAs? •DNA methylation? •Patient stratification •Improved diagnosis and prognosis •Prediction of therapeutic outcome •Novel therapeutic strategies •Drug repositioning •Personal Big data •Real-time monitoring •Biometrics Improved clinical decision-making Disease prevention Healthcare quality assurance and cost-effectiveness Improved health maintenance Figure 1. A to D. Schematic representation of how the use of healthcare-relevant Big Data in MS could lead to the application of Precision and Personalized medicine in the routine clinical practice. E. Schematic representation of a Deep Learning approach. For example, from a FLAIR sequence, the Convolutional Neural Network is able to recognize the location of the lesion. This same process merged with clinical, laboratory and genetic data, would result in a powerful prediction tool. Environmental •Epstein Barr virus •Cigarrete smoking •Vitamin D •Sun exposure •Latitude gradient •Microbiote? Genetic •HLA-DRB1 •Non-HLA variants (≈ 200) Epigenetic •Micro RNAs? •DNA methylation? Catalonia: 400 new patients by year Girona: 35 new patients by year 2.5 MILLIONS Worldwide 700,000 Europe 47,000 Spain 7,000 Catalonia 900 Girona Global MS prevalence 65-125 cases / 100,000 inhabitants Predictive Models Based on Deep Learning using Radiological, Clinical, Laboratory and Genetic Data Big-Data in Multiple Sclerosis: Robles-Cedeño, René1,2,3 ; Perkal Rug, Héctor2,3 ; Quintana Camps Ester2,3 ; Gich Fullà, Jordi1,2,3 ; Ramió-Torrentà, Lluís1,2,3 1 Department of Neurology, Dr. Josep Trueta University Hospital and Santa Caterina Hospital, Girona-Spain. 2 Girona Neuroimmunology and Multiple Sclerosis Unit, Neurology Department, Dr. Josep Trueta University Hospital. Biomedical Research Institute (IDIBGI), Girona, Spain. 3 Neurodegeneration and Neuroinflammation Group. Girona Biomedical Research Institute (IDIBGI), Girona-Spain. uniem.girona.ics@gencat.cat @UNIEMGirona 1 Multiple sclerosis (MS) is a chronic and disabling disease of the central nervous system (CNS) affecting young adults and inducing a progressive deterioration in the motor and cognitive spheres. 2 Pathogenesis of MS is not completely known. The interaction of environmental and epigenetic factors in genetically susceptible individuals would trigger the disease. 3 Many efforts are focusing on identifying biomarkers that allow us to optimize the management and improve the prognosis. 4 Our hypothesis here is that, by using the available computer power together with a new set of Artificial Intelligence and Big Data techniques (Deep Learning), new biomarkers and deep predictive models of the disease evolution can be proposed using not only those from rich MRI descriptors but also fusing them with available environmental, clinical, laboratory and genetic information. 5 In MS, the results of this project could give us the keys to start the use of such Deep Learning techniques in the routine clinical practice Advances in this line will allow us to transfer such knowledge into predictive models about the evolution of the disease or the probability of response to a particular drug or medical intervention which could markedly change the current paradigms of diagnosis, prognosis and treatment of MS in the near future.