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Deep learning for brain disorders: from data processing to disease treatment

Authors :
Elina Thibeau-Sutre
Ninon Burgos
Olivier Colliot
Simona Bottani
Johann Faouzi
Algorithms, models and methods for images and signals of the human brain (ARAMIS)
Sorbonne Université (SU)-Inria de Paris
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM)
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP]
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP]
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d'avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), from the ICM Big Brain Theory Program (project PredictICD), and from the Abeona Foundation (project Brain@Scale).
ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Colliot, Olivier
PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM)
Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP]
Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP]
Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Source :
Briefings in Bioinformatics, Briefings in Bioinformatics, 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩, Briefings in Bioinformatics, Oxford University Press (OUP), 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Details

Language :
English
ISSN :
14675463 and 14774054
Database :
OpenAIRE
Journal :
Briefings in Bioinformatics, Briefings in Bioinformatics, 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩, Briefings in Bioinformatics, Oxford University Press (OUP), 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩
Accession number :
edsair.doi.dedup.....0d66b35549733d9883e64391e8e81702