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AIM in Amyotrophic Lateral Sclerosis

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Artificial Intelligence in Medicine

Abstract

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disease of motor neurons with substantial heterogeneity in its clinical presentation. Survival ranges from 3 to 5 years after symptom onset depending on genetic, geographic, and phenotypic factors. Despite tireless research efforts, the cause of ALS remains unknown, and therapy development efforts are confounded by the lack of accurate prognosis markers. Artificial intelligence (AI) with machine learning (ML) methods offers unprecedented opportunities to construct accurate prognosis and diagnostic models. Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset is the largest public ALS clinical trial data that various researches were accomplished on this framework with ML approach. In this chapter, we study the causal analysis of ALS with AI perspective, and also ML methods for predicting the evolution of ALS will be explained on the PRO-ACT dataset. This study reveals that AI-based longitudinal study of ALS by considering clinical, genetic, and imaging factors could bring new insight to the core etiology of ALS.

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Acknowledgments

This work was supported by the Italian Ministry of Health (Ministero della Salute, Ricerca Sanitaria Finalizzata, grant RF-2016-02362405); the Progetti di Rilevante Interesse Nazionale program of the Ministry of Education, University, and Research (grant 2017SNW5MB); the European Commission’s Health Seventh Framework Programme (FP7/2007–2013 under grant agreement 259867); and the Joint Programme-Neurodegenerative Disease Research (Strength, ALS-Care, and Brain-Mend projects), granted by Italian Ministry of Education, University and Research. This study was performed under the Department of Excellence grant of the Italian Ministry of Education, University, and Research to the “Rita Levi Montalcini” Department of Neuroscience, University of Torino, Italy.

Declaration of Competing Interests

Adriano Chiò serves on the Scientific Advisory Board for Mitsubishi Tanabe, Roche, Biogen, Denali, and Cytokinetics.

Author information

Authors and Affiliations

  1. Computer Engineering – Artificial Intelligence, Iran University of Science and Technology, Tehran, Iran

    Meysam Ahangaran

  2. Mazandaran University of Science and Technology, Babol, Iran

    Meysam Ahangaran

  3. ‘Rita Levi Montalcini’ Department of Neuroscience, University of Turin, Turin, Italy

    Adriano Chiò

Authors
  1. Meysam Ahangaran
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  2. Adriano Chiò
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Editor information

Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Ahangaran, M., Chiò, A. (2021). AIM in Amyotrophic Lateral Sclerosis. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_252-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_252-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58080-3

  • Online ISBN: 978-3-030-58080-3

  • eBook Packages: Springer Reference MedicineReference Module Medicine

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