Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review
Abstract Introduction The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the bi...
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| Format: | Article |
| Language: | English |
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BMC
2021-06-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-021-00896-2 |
| _version_ | 1819066580497072128 |
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| author | Felipe Fernandes Ingridy Barbalho Daniele Barros Ricardo Valentim César Teixeira Jorge Henriques Paulo Gil Mário Dourado Júnior |
| author_facet | Felipe Fernandes Ingridy Barbalho Daniele Barros Ricardo Valentim César Teixeira Jorge Henriques Paulo Gil Mário Dourado Júnior |
| author_sort | Felipe Fernandes |
| collection | DOAJ |
| description | Abstract Introduction The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. Methods This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. Discussions Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). Conclusions Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS. |
| first_indexed | 2024-12-21T16:04:37Z |
| format | Article |
| id | doaj.art-28e29f40840a491b8feb6c076a84a22b |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-21T16:04:37Z |
| publishDate | 2021-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-28e29f40840a491b8feb6c076a84a22b2022-12-21T18:57:54ZengBMCBioMedical Engineering OnLine1475-925X2021-06-0120112210.1186/s12938-021-00896-2Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic reviewFelipe Fernandes0Ingridy Barbalho1Daniele Barros2Ricardo Valentim3César Teixeira4Jorge Henriques5Paulo Gil6Mário Dourado Júnior7Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN)Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN)Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN)Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN)Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of CoimbraDepartment of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of CoimbraDepartment of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of CoimbraLaboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN)Abstract Introduction The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. Methods This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. Discussions Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). Conclusions Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.https://doi.org/10.1186/s12938-021-00896-2Amyotrophic lateral sclerosis—ALSArtificial intelligenceBiomedical signalsChronic neurological conditionsMachine learningMotor neuron disease |
| spellingShingle | Felipe Fernandes Ingridy Barbalho Daniele Barros Ricardo Valentim César Teixeira Jorge Henriques Paulo Gil Mário Dourado Júnior Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review BioMedical Engineering OnLine Amyotrophic lateral sclerosis—ALS Artificial intelligence Biomedical signals Chronic neurological conditions Machine learning Motor neuron disease |
| title | Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review |
| title_full | Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review |
| title_fullStr | Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review |
| title_full_unstemmed | Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review |
| title_short | Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review |
| title_sort | biomedical signals and machine learning in amyotrophic lateral sclerosis a systematic review |
| topic | Amyotrophic lateral sclerosis—ALS Artificial intelligence Biomedical signals Chronic neurological conditions Machine learning Motor neuron disease |
| url | https://doi.org/10.1186/s12938-021-00896-2 |
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