Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2023 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10451/58709 |
Resumo: | © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). |
| id |
RCAP_8308fa7eafdce706a2f0fa5443888dfa |
|---|---|
| oai_identifier_str |
oai:repositorio.ul.pt:10451/58709 |
| network_acronym_str |
RCAP |
| network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository_id_str |
7160 |
| spelling |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic reviewAmyotrophic lateral sclerosisArtificial intelligenceDisease progressionPredictionStratificationSystematic review© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/).Background: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. Objective: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. Methods: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. Results: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. Conclusion: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.ElsevierRepositório da Universidade de LisboaTavazzi, EricaLongato, EnricoVettoretti, MartinaAidos, HelenaTrescato, IsottaRoversi, ChiaraMartins, Andreia S.Castanho, Eduardo N.Branco, RubenSoares, Diogo F.Guazzo, AlessandroBirolo, GiovanniPala, DanieleBosoni, PietroChiò, AdrianoManera, UmbertoCarvalho, MamedeAndré e Silva Miranda, BrunoGromicho, MartaAlves, InêsBellazzi, RiccardoDagliati, AriannaFariselli, PieroMadeira, Sara C.Di Camillo, Barbara2023-07-21T11:39:18Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/58709engArtif Intell Med. 2023 Aug;142:1025880933-365710.1016/j.artmed.2023.1025881873-2860info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:07:32Zoai:repositorio.ul.pt:10451/58709Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:08:49.724415Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| title |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| spellingShingle |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review Tavazzi, Erica Amyotrophic lateral sclerosis Artificial intelligence Disease progression Prediction Stratification Systematic review |
| title_short |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| title_full |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| title_fullStr |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| title_full_unstemmed |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| title_sort |
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review |
| author |
Tavazzi, Erica |
| author_facet |
Tavazzi, Erica Longato, Enrico Vettoretti, Martina Aidos, Helena Trescato, Isotta Roversi, Chiara Martins, Andreia S. Castanho, Eduardo N. Branco, Ruben Soares, Diogo F. Guazzo, Alessandro Birolo, Giovanni Pala, Daniele Bosoni, Pietro Chiò, Adriano Manera, Umberto Carvalho, Mamede André e Silva Miranda, Bruno Gromicho, Marta Alves, Inês Bellazzi, Riccardo Dagliati, Arianna Fariselli, Piero Madeira, Sara C. Di Camillo, Barbara |
| author_role |
author |
| author2 |
Longato, Enrico Vettoretti, Martina Aidos, Helena Trescato, Isotta Roversi, Chiara Martins, Andreia S. Castanho, Eduardo N. Branco, Ruben Soares, Diogo F. Guazzo, Alessandro Birolo, Giovanni Pala, Daniele Bosoni, Pietro Chiò, Adriano Manera, Umberto Carvalho, Mamede André e Silva Miranda, Bruno Gromicho, Marta Alves, Inês Bellazzi, Riccardo Dagliati, Arianna Fariselli, Piero Madeira, Sara C. Di Camillo, Barbara |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
| dc.contributor.author.fl_str_mv |
Tavazzi, Erica Longato, Enrico Vettoretti, Martina Aidos, Helena Trescato, Isotta Roversi, Chiara Martins, Andreia S. Castanho, Eduardo N. Branco, Ruben Soares, Diogo F. Guazzo, Alessandro Birolo, Giovanni Pala, Daniele Bosoni, Pietro Chiò, Adriano Manera, Umberto Carvalho, Mamede André e Silva Miranda, Bruno Gromicho, Marta Alves, Inês Bellazzi, Riccardo Dagliati, Arianna Fariselli, Piero Madeira, Sara C. Di Camillo, Barbara |
| dc.subject.por.fl_str_mv |
Amyotrophic lateral sclerosis Artificial intelligence Disease progression Prediction Stratification Systematic review |
| topic |
Amyotrophic lateral sclerosis Artificial intelligence Disease progression Prediction Stratification Systematic review |
| description |
© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-07-21T11:39:18Z 2023 2023-01-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/58709 |
| url |
http://hdl.handle.net/10451/58709 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
Artif Intell Med. 2023 Aug;142:102588 0933-3657 10.1016/j.artmed.2023.102588 1873-2860 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
| instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| repository.mail.fl_str_mv |
|
| _version_ |
1799134643615694848 |