Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

S Demuth; J Paris; I Faddeenkov; J De Sèze; P-A Gourraud

doi:10.1016/j.neurol.2024.04.004

Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

Rev Neurol (Paris). 2024 May 20:S0035-3787(24)00522-8. doi: 10.1016/j.neurol.2024.04.004. Online ahead of print.

Authors

S Demuth¹, J Paris², I Faddeenkov², J De Sèze³, P-A Gourraud⁴

Affiliations

¹ Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France. Electronic address: stanislas.demuth@chru-strasbourg.fr.
² Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France.
³ Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France; Department of Neurology, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France; Inserm CIC 1434 Clinical Investigation Center, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France.
⁴ Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; "Data clinic", Department of Public Health, University Hospital of Nantes, Nantes, France.

PMID: 38772806
DOI: 10.1016/j.neurol.2024.04.004

Abstract

Background: Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.

Objectives: Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.

Methods: We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.

Results: The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.

Conclusion: The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.

Keywords: Artificial Intelligence; Deep learning; Multiple Sclerosis; NMOSD; Neuroimmunology.

Publication types

Review