Subscribe to RSS
DOI: 10.1590/0004-282X-ANP-2022-S139
Artificial intelligence and Big Data in neurology
Inteligência artificial e Big Data em neurologia
ABSTRACT
Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.
RESUMO
Os recentes avanços na tecnologia nos permitiram acessar uma infinidade de conjuntos de dados pertencentes a várias dimensões da neurologia. Juntamente com as enormes oportunidades, também enfrentamos desafios relacionados à qualidade dos dados, ética e dificuldades intrínsecas relacionadas à aplicação da ciência de dados na área da saúde. Neste artigo descreveremos os principais avanços no campo da inteligência artificial e Big Data aplicados à neurologia com foco nas neurociências baseadas em imagens médicas. Dados do mundo real (RWD) e análises relacionadas ao grande volume de informações serão descritos, bem como algumas das iniciativas científicas mais relevantes no momento da redação deste artigo.
Publication History
Received: 06 April 2022
Accepted: 29 April 2022
Article published online:
06 February 2023
© 2022. Academia Brasileira de Neurologia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Thieme Revinter Publicações Ltda.
Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil
-
References
- 1 Landhuis E. Neuroscience: Big brain, big data. Nature 2017; 541 7638 559-561 https://doi.org/10.1038/541559a
- 2 Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data 2019; 6 (01) 54-54 https://doi.org/10.1186/s40537-019-0217-0
- 3 Falk TH, Sejdić E. Signal Processing and Machine Learning for Biomedical Big Data. 1. Boca Raton: CRC Press; 2018: 624-624 https://doi.org/10.1201/9781351061223-1
- 4 Natarajan P, Frenzel JC, Smaltz DH, Mukherjee P. Demystifying Big Data and Machine Learning for Healthcare. 1. Boca Raton: CRC Press; 2017: 210-210
- 5 Muthukrishnan N, Maleki F, Ovens K, Reinhold C, Forghani B, Forghani R. Brief History of Artificial Intelligence. Neuroimag Clin N Am 2020; 30 (04) 393-399 https://doi.org/10.1016/j.nic.2020.07.004
- 6 Dale R. GPT-3: What’s it good for?. Nat Lang Eng 2021; 27 (01) 113-118 https://doi.org/10.1017/s1351324920000601
- 7 Deliberato RO, Escudero GG, Bulgarelli L. et al. SEVERITAS_ An externally validated mortality prediction for critically ill patients in low and middle-income countries. Int J Med Inform 2019; 131: 103959-103959 https://doi.org/10.1016/j.ijmedinf.2019.103959
- 8 Batista AF de M, Miraglia JL, Donato THR, ADP Filho Chiavegatto . COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv 2020; 1-8 https://doi.org/10.1101/2020.04.04.20052092
- 9 Oliveira Jr PP, Nitrini R, Busatto G, Buchpiguel C, Sato Jr JR. Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer’s Disease. J Alzheimer’s Dis 2010; 19 (04) 1263-1272 https://doi.org/10.3233/jad-2010-1322
- 10 Ushizima D, Chen Y, Alegro M. et al. Deep learning for Alzheimer’s disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation. Neuroimage 2022; 248: 118790-118790 https://doi.org/10.1016/j.neuroimage.2021.118790
- 11 Awad A, Bader-El-Den M, McNicholas J. Patient length of stay and mortality prediction: A survey. Heal Serv Management Res 2017; 30 (02) 105-120 https://doi.org/10.1177/0951484817696212
- 12 Sejnowski TJ, Churchland PS, Movshon JA. Putting big data to good use in neuroscience. Nat Neurosci 2014; 17 (11) 1440-1441 https://doi.org/10.1038/nn.3839
- 13 Shen G, Horikawa T, Majima K, Kamitani Y. Deep image reconstruction from human brain activity. Biorxiv 2017; 240317-240317 https://doi.org/10.1101/240317
- 14 Fothergill BT, Knight W, Stahl BC, Ulnicane I. Responsible Data Governance of Neuroscience Big Data. Front Neuroinform 2019; 13: 28-28 https://doi.org/10.3389/fninf.2019.00028
- 15 Davatzikos C. Machine learning in neuroimaging: Progress and challenges. Neuroimage 2019; 197: 652-656 https://doi.org/10.1016/j.neuroimage.2018.10.003
- 16 Charpentier CJ, Faulkner P, Pool ER. et al. How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between fMRI and behaviour-only research participants. Soc Cogn Affect Neur 2021; 16 (10) 1057-1070 https://doi.org/10.1093/scan/nsab057
- 17 Hibar DP, Stein JL, Renteria ME. et al. Common genetic variants influence human subcortical brain structures. Nature 2015; 520 7546 224-229 https://doi.org/10.1038/nature14101
- 18 Bethlehem RAI, Seidlitz J, White SR. et al. Brain charts for the human lifespan. Nature 2022; 604 7906 525-533 https://doi.org/10.1101/2021.06.08.447489
- 19 Williams R. The human connectome: just another ’ome?. Lancet Neurology 2010; 9 (03) 238-239 https://doi.org/10.1016/s1474-4422(10)70046-6
- 20 Jack CR, Bernstein MA, Fox NC. et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 2008; 27 (04) 685-691 https://doi.org/10.1002/jmri.21049
- 21 Ollier W, Sprosen T, Peakman T. UK Biobank: from concept to reality. Pharmacogenomics 2005; 6 (06) 639-646 https://doi.org/10.2217/14622416.6.6.639
- 22 Petersen SE, Matthews PM, Bamberg F. et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches. J Cardiov Magn Reson 2013; 15 (01) 46-46 https://doi.org/10.1186/1532-429x-15-46
- 23 Okano H, Miyawaki A, Kasai K. Brain/MINDS: brain-mapping project in Japan. Philos Trans R Soc Lond B Biol Sci 2015; 370 1668 20140310-20140310 https://doi.org/10.1098/rstb.2014.0310
- 24 Volkow ND, Koob GF, Croyle RT. et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev Cogn Neuros-neth 2018; 32: 4-7 https://doi.org/10.1016/j.dcn.2017.10.002
- 25 Valdes-Sosa PA, Galan-Garcia L, Bosch-Bayard J. et al. The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset. Sci Data 2021; 8 (01) 45-45 https://doi.org/10.1038/s41597-021-00829-7
- 26 Thompson PM, Jahanshad N, Schmaal L. et al. The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping. Hum Brain Mapp 2022; 43 (01) 15-22 https://doi.org/10.1002/hbm.25672
- 27 Naslavsky MS, Scliar MO, Yamamoto GL. et al. Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil. Nat Commun 2022; 13 (01) 1004-1004 https://doi.org/10.1038/s41467-022-28648-3
- 28 Rodrigues MAS, Rodrigues TP, Zatz M. et al. Quantitative evaluation of brain volume among elderly individuals in São Paulo, Brazil: a population-based study. Radiol Bras 2019; 52 (05) 293-298 https://doi.org/10.1590/0100-3984.2018.0074
- 29 Serpa Neto A, Kugener G, Bulgarelli L. et al. First Brazilian datathon in critical care. Rev Bras Ter Intensiva 2018; 30 (01) 6-8 https://doi.org/10.5935/0103-507x.20180006
- 30 Piza FM de T, Celi LA, Deliberato RO. et al. Assessing team effectiveness and affective learning in a datathon. Int J Med Inform 2018; 112: 40-44 https://doi.org/10.1016/j.ijmedinf.2018.01.005