![]() While no unifying pathogenesis has been described across the entire spectrum of ALS phenotypes, the incidence of the condition is projected to rise in the next couple of decades ( Arthur et al., 2016) highlighting the urgency of drug development and translational research. An estimated 450 000 people are affected by ALS worldwide according to the ALS Therapy Development Institute. In Europe, its incidence varies between 2 or 3 cases per 100 000 individuals ( Hardiman et al., 2017) and its prevalence is between 5 and 8 cases per 100 000 ( Chiò et al., 2013b). The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.Īmyotrophic Lateral Sclerosis (ALS) is an adult-onset multi-system neurodegenerative condition with predominant motor system involvement. ![]() Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated.Ĭonclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. These models also offer patient stratification opportunities for future clinical trials. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. 8Computational Neuroimaging Group, Trinity College, Dublin, Irelandīackground: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present.7Modal'X, Paris Nanterre University, Nanterre, France.6Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France.5Northern Ireland Center for Stratified Medecine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Altnagelvin Hospital, Londonderry, United Kingdom.4APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France.3Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France.1Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France.Vincent Grollemund 1,2 *, Pierre-François Pradat 3,4,5, Giorgia Querin 3,4, François Delbot 1,6, Gaétan Le Chat 2, Jean-François Pradat-Peyre 1,6,7 and Peter Bede 3,4,8
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