{"id":24726,"date":"2023-10-18T11:00:05","date_gmt":"2023-10-18T11:00:05","guid":{"rendered":"https:\/\/natus.com\/insights\/5-key-benefits-artificial-intelligence-for-eeg\/"},"modified":"2025-08-26T15:22:34","modified_gmt":"2025-08-26T15:22:34","slug":"5-avantages-cles-de-lintelligence-artificielle-pour-leeg","status":"publish","type":"insights","link":"https:\/\/natus.com\/fr\/insights\/5-avantages-cles-de-lintelligence-artificielle-pour-leeg\/","title":{"rendered":"5 avantages cl\u00e9s de l\u2019intelligence artificielle pour l\u2019EEG"},"content":{"rendered":"","protected":false},"author":2,"template":"","insight_type":[319],"insights_category":[446],"insights_tag":[481],"class_list":["post-24726","insights","type-insights","status-publish","hentry","insight_type-neuro","insights_category-eeg","insights_tag-ai-fr"],"acf":{"content_blocks":[{"acf_fc_layout":"hero_insights","_acfe_flexible_toggle":"","hero_insights":{"module_id":"n651a1bdf6995b","module_class":"","background_color":"#00aaa7","intro":"","h1":"5 avantages cl\u00e9s de l\u2019intelligence artificielle pour l\u2019EEG","insights_image":{"ID":14888,"id":14888,"title":"Benefits of AI_Insights 1300x500","filename":"Benefits-of-AI_Insights-1300x500-1.png","filesize":1193770,"url":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","link":"https:\/\/natus.com\/fr\/insights\/5-avantages-cles-de-lintelligence-artificielle-pour-leeg\/benefits-of-ai_insights-1300x500-2\/","alt":"5 key benefits of artificial intelligence for EEG","author":"2","description":"","caption":"","name":"benefits-of-ai_insights-1300x500-2","status":"inherit","uploaded_to":24726,"date":"2023-10-12 20:47:21","modified":"2024-07-30 14:59:39","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/natus.com\/wp-includes\/images\/media\/default.png","width":1300,"height":500,"sizes":{"thumbnail":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","thumbnail-width":128,"thumbnail-height":49,"medium":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","medium-width":1300,"medium-height":500,"medium_large":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1-768x295.png","medium_large-width":768,"medium_large-height":295,"large":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","large-width":1300,"large-height":500,"1536x1536":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","1536x1536-width":1300,"1536x1536-height":500,"2048x2048":"https:\/\/natus.com\/wp-content\/uploads\/Benefits-of-AI_Insights-1300x500-1.png","2048x2048-width":1300,"2048x2048-height":500}}}},{"acf_fc_layout":"simple_content","_acfe_flexible_toggle":"","content_full_width_landing":{"module_options":{"":null,"module_id":"n65235aa0431c8","module_class":"","module_background_type":"color","module_background_color":"#f1f1f1","module_background_image":false,"module_background_video":"","activate_custom_padding":false,"padding_top_desktop":0,"padding_top_tablet":"","padding_top_mobile":"","padding_bottom_desktop":"","padding_bottom_tablet":"","padding_bottom_mobile":"","activate_custom_margin":false,"margin_top_desktop":"","margin_top_tablet":"","margin_top_mobile":"","margin_bottom_desktop":"","margin_bottom_tablet":"","margin_bottom_mobile":"","disable_on":[],"content_alignment_desktop":"left","content_alignment_tablet":"left","content_alignment_mobile":"left"},"content":"<p>Dans le contexte d\u2019\u00e9volution rapide de l\u2019informatique dans le domaine de la sant\u00e9, l\u2019IA est apparue comme une force centrale pour aider les professionnels de sant\u00e9 \u00e0 devenir plus efficaces et performants. L\u2019adoption de l\u2019IA dans le secteur m\u00e9dical a vari\u00e9 selon la sp\u00e9cialit\u00e9 et l\u2019application, certains domaines progressant plus rapidement que d\u2019autres. Depuis de nombreuses ann\u00e9es, la cardiologie, par exemple, utilise l\u2019IA pour les tests d\u2019ECG et d\u2019imagerie afin de d\u00e9tecter plus facilement les anomalies cardiaques subtiles et de fournir des \u00e9valuations plus rapides. Il en va de m\u00eame pour la radiologie et la mammographie, o\u00f9 l\u2019<a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7592467\/\" target=\"_blank\" rel=\"noopener\">utilisation de la technologie d\u2019IA s\u2019est intensifi\u00e9e au cours de la derni\u00e8re d\u00e9cennie<\/a>.1<sup>1<\/sup><\/p>\n<p>&nbsp;<\/p>\n<p>L\u2019IA progresse \u00e9galement de mani\u00e8re significative dans le domaine de la neurologie, o\u00f9 de nouveaux outils utilisant <a href=\"https:\/\/jamanetwork.com\/journals\/jamaneurology\/fullarticle\/2806244\" target=\"_blank\" rel=\"noopener\">l\u2019apprentissage profond pour interpr\u00e9ter et analyser les donn\u00e9es EEG\u00b2 <\/a> offrent un grand potentiel. De nombreux experts estiment que l\u2019int\u00e9gration de la technologie de neuro-IA aura un impact plus important que de nombreux autres cas d\u2019utilisation de l\u2019IA dans le domaine m\u00e9dical. Ceci est li\u00e9 \u00e0 de nombreux facteurs concernant l\u2019interpr\u00e9tation et l\u2019analyse de l\u2019EEG, tels que :<\/p>\n<ul>\n<li>Le grand nombre de t\u00e2ches r\u00e9p\u00e9titives, voire fastidieuses, impliqu\u00e9es dans l\u2019analyse de l\u2019EEG.<\/li>\n<li>Une p\u00e9nurie de personnel qualifi\u00e9, particuli\u00e8rement dans les r\u00e9gions \u00e9loign\u00e9es et mal desservies.<\/li>\n<li>Il y a un besoin de normalisation de la notation de l\u2019interpr\u00e9tation de l\u2019EEG.<\/li>\n<li>La complexit\u00e9 et la nature non lin\u00e9aire des signaux d\u2019EEG.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Toutefois, certains obstacles subsistent \u00e0 l\u2019adoption rapide des outils de neuro IA. <a href=\"https:\/\/natus.com\/insights\/4-reasons-neurologists-can-trust-ai-for-eeg\/\">La m\u00e9fiance envers la technologie de l\u2019IA est un facteur important qui doit \u00eatre surmont\u00e9.<\/a> Davantage de protocoles pour l\u2019int\u00e9gration de l\u2019IA, ainsi qu\u2019une formation pour les neurologues, les \u00e9pileptologues, le personnel neurodiagnostique et d\u2019autres professionnels, renforceront la confiance en cette technologie.<\/p>\n<p>&nbsp;<\/p>\n<p>La recherche d\u00e9montre \u00e9galement qu\u2019il est essentiel de <a href=\"https:\/\/www.nature.com\/articles\/s41746-022-00597-7\" target=\"_blank\" rel=\"noopener\">surmonter la perception selon laquelle l\u2019IA remplacera l\u2019expertise<\/a>\u00b3 pour l\u2019adoption de l\u2019IA \u00e0 grande \u00e9chelle dans l\u2019interpr\u00e9tation de l\u2019EEG. Les \u00e9quipes de neurologie doivent consid\u00e9rer les outils d\u2019IA comme des assistants utiles et fiables qui permettent de gagner du temps, d\u2019accro\u00eetre la pr\u00e9cision et d\u2019am\u00e9liorer les soins aux patients, plut\u00f4t que comme un remplacement de l\u2019expertise humaine. Lorsque ce changement de mentalit\u00e9 se produit, les \u00e9quipes de soins neurologiques peuvent pleinement tirer parti de la collaboration homme-machine, o\u00f9 l\u2019IA lib\u00e8re les m\u00e9decins et d\u2019autres personnes pour qu\u2019ils se concentrent sur les t\u00e2ches les plus cruciales en mati\u00e8re de diagnostic et de traitement des anomalies et des affections c\u00e9r\u00e9brales.<\/p>\n<p>Une fois ces obstacles \u00e0 l\u2019adoption surmont\u00e9s, l\u2019int\u00e9gration de l\u2019IA en tant que technologie de soutien \u00e0 l\u2019interpr\u00e9tation de l\u2019EEG peut avoir des effets extr\u00eamement positifs sur l\u2019efficacit\u00e9, les co\u00fbts et, \u00e0 terme, les r\u00e9sultats pour les patients. Bien que l\u2019IA ait de nombreuses <a href=\"https:\/\/pages.natus.com\/ai-for-eeg-interpretation\" target=\"_blank\" rel=\"noopener\">applications pratiques pour l\u2019EEG<\/a>, cet article r\u00e9sume cinq des raisons les plus convaincantes pour lesquelles l\u2019IA a un potentiel immense pour l\u2019analyse de l\u2019EEG.<\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008b96;\">1. Exactitude et pr\u00e9cision am\u00e9lior\u00e9es <\/span><\/h4>\n<p>L\u2019interpr\u00e9tation traditionnelle de l\u2019EEG repose fortement sur l\u2019 expertise humaine pour des t\u00e2ches fastidieuses et r\u00e9p\u00e9titives. M\u00eame les cliniciens les plus exp\u00e9riment\u00e9s peuvent mal interpr\u00e9ter des sch\u00e9mas subtils ou n\u00e9gliger des d\u00e9tails critiques dans des donn\u00e9es EEG denses, ce qui conduit \u00e0 des r\u00e9sultats incoh\u00e9rents, voire \u00e0 un diagnostic erron\u00e9. Les algorithmes d\u2019apprentissage automatique peuvent \u00eatre entra\u00een\u00e9s sur de vastes ensembles de donn\u00e9es contenant divers sch\u00e9mas d\u2019EEG, ce qui permet une d\u00e9tection plus rapide des anomalies, des motifs et des irr\u00e9gularit\u00e9s. Des outils d\u2019IA encore plus avanc\u00e9s qui utilisent des algorithmes d\u2019apprentissage profond peuvent traiter et analyser simultan\u00e9ment des relations temporelles et spatiales complexes dans les donn\u00e9es EEG.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008b96;\">2. R\u00e9sultats standardis\u00e9s<\/span><\/h4>\n<p>Les m\u00e9thodes traditionnelles d\u2019interpr\u00e9tation de l\u2019EEG sont souvent subjectives et qualitatives, sans <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/24531133\/\">norme universelle largement utilis\u00e9e dans les pratiques d\u2019EEG d\u2019aujourd\u2019hui<\/a>.<sup>4<\/sup> Ce probl\u00e8me est exacerb\u00e9 par le fait que l\u2019expertise en EEG n\u2019est pas toujours facilement disponible, et m\u00eame les sp\u00e9cialistes exp\u00e9riment\u00e9s peuvent ne pas avoir re\u00e7u de formation sp\u00e9cialis\u00e9e. La capacit\u00e9 de former des algorithmes d\u2019IA sur des ensembles de donn\u00e9es extr\u00eamement vastes comprenant un large \u00e9ventail de mod\u00e8les d\u2019EEG permet une standardisation dans diverses sp\u00e9cialit\u00e9s et populations de patients. Bien que les algorithmes traditionnels n\u00e9cessitent un effort manuel pour affiner leur utilisation sur un ensemble de donn\u00e9es ou un groupe sp\u00e9cifique, les algorithmes d\u2019IA peuvent traiter de vastes ensembles de donn\u00e9es de mani\u00e8re objective et coh\u00e9rente. Cela aide \u00e0 encourager l\u2019uniformit\u00e9 dans les rapports d\u2019EEG et \u00e0 \u00e9tablir des points de r\u00e9f\u00e9rence pour \u00e9valuer l\u2019\u00e9tat de chaque patient au fil du temps.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008b96;\">3. Un soutien plus collaboratif \u00e0 la d\u00e9cision<\/span><\/h4>\n<p><a href=\"https:\/\/pages.natus.com\/multidisciplinary-approach-neonates-in-the-nicu\">Une approche multidisciplinaire<\/a> est plus efficace dans le paysage actuel des soins de sant\u00e9. Le traitement des patients en neurologie requiert souvent la contribution de grandes \u00e9quipes de neurologues, d\u2019\u00e9pileptologues, de professionnels du neurodiagnostic et d\u2019autres sp\u00e9cialistes. L\u2019informatique assist\u00e9e par l\u2019IA peut trier et examiner rapidement les donn\u00e9es, et produire une s\u00e9rie d\u2019interpr\u00e9tations, d\u2019\u00e9valuations de risques, de probabilit\u00e9s de traitements et de donn\u00e9es statistiques d\u00e9riv\u00e9es des ant\u00e9c\u00e9dents m\u00e9dicaux du patient en conjonction avec des ensembles de donn\u00e9es de rapports d\u2019EEG existants et valid\u00e9s. En fait, la recherche rapporte que <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9601264\">la collaboration entre humains et machines intelligentes est devenue une caract\u00e9ristique fondamentale \u00e0 la r\u00e9ussite d\u2019un syst\u00e8me d\u2019aide \u00e0 la d\u00e9cision clinique.<sup>5<\/sup> M\u00eame avec une technologie avanc\u00e9e d\u2019IA, les perspectives humaines qualitatives restent cruciales pour la r\u00e9ussite des syst\u00e8mes d\u2019aide \u00e0 la d\u00e9cision complexes.<\/a><\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008b96;\">4. Efficacit\u00e9 accrue<\/span><\/h4>\n<p>L\u2019int\u00e9gration de l\u2019IA dans l\u2019interpr\u00e9tation des EEG inaugure une nouvelle \u00e8re d\u2019efficacit\u00e9 et de rapidit\u00e9. Le volume consid\u00e9rable de donn\u00e9es g\u00e9n\u00e9r\u00e9 par les enregistrements EEG peut submerger l\u2019interpr\u00e9tation manuelle. Les algorithmes d\u2019IA peuvent rapidement analyser ces donn\u00e9es, en mettant en \u00e9vidence les segments susceptibles de pr\u00e9senter une anomalie. Cette approche cibl\u00e9e r\u00e9duit la charge des examinateurs humains et permet de s\u2019assurer qu\u2019aucune information cruciale ne passe inaper\u00e7ue. De plus, l\u2019IA peut traiter des enregistrements prolong\u00e9s qui pourraient \u00eatre difficiles \u00e0 \u00e9valuer pour les examinateurs humains, am\u00e9liorant ainsi la qualit\u00e9 globale de l\u2019analyse de l\u2019EEG. Les capacit\u00e9s de traitement rapide de l\u2019IA peuvent acc\u00e9l\u00e9rer l\u2019analyse de l\u2019EEG, permettant ainsi aux cliniciens de consacrer leur temps pr\u00e9cieux \u00e0 l\u2019examen des cas critiques et \u00e0 l\u2019\u00e9laboration de plans de traitement. Les cas de routine peuvent \u00eatre trait\u00e9s efficacement par des algorithmes d\u2019IA, permettant aux neurologues et autres cliniciens de se concentrer davantage sur les cas complexes. Ceci est particuli\u00e8rement utile dans les r\u00e9gions \u00e9loign\u00e9es et mal desservies o\u00f9 l\u2019expertise en EEG est rare ou tout simplement indisponible.<\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008b96;\">5. Un coup de pouce aux r\u00e9sultats financiers<\/span><\/h4>\n<p>D\u2019une mani\u00e8re g\u00e9n\u00e9rale, l\u2019adoption de l\u2019IA dans le secteur de la sant\u00e9 au cours des cinq prochaines ann\u00e9es a \u00e9t\u00e9 <a href=\"https:\/\/cepr.org\/voxeu\/columns\/what-happens-when-ai-comes-healthcare\">pr\u00e9dite pour r\u00e9duire les co\u00fbts de 360 milliards de dollars<\/a>.<sup>7 <\/sup> Les experts attribuent une grande partie de ces \u00e9conomies \u00e0 l\u2019augmentation de la productivit\u00e9 du travail, en particulier \u00e0 mesure que les \u00e9quipes homme\/machine se g\u00e9n\u00e9ralisent. L\u2019int\u00e9gration r\u00e9fl\u00e9chie de la technologie IA peut optimiser le travail des neurologues, \u00e9pileptologues et autres cliniciens qualifi\u00e9s en r\u00e9duisant les ressources consacr\u00e9es \u00e0 des t\u00e2ches d\u2019interpr\u00e9tation EEG r\u00e9p\u00e9titives, co\u00fbteuses et chronophages.<\/p>\n<p>&nbsp;<\/p>\n<p>Alors que nous envisageons l\u2019avenir des soins de sant\u00e9, le potentiel transformateur de l\u2019IA dans l\u2019interpr\u00e9tation et l\u2019analyse de l\u2019EEG est ind\u00e9niable. La capacit\u00e9 de l\u2019IA neuro \u00e0 am\u00e9liorer la pr\u00e9cision, \u00e0 am\u00e9liorer l\u2019interpr\u00e9tation standardis\u00e9e, \u00e0 soutenir la prise de d\u00e9cision collaborative, \u00e0 accro\u00eetre la rentabilit\u00e9 et \u00e0 acc\u00e9l\u00e9rer les processus remod\u00e8le notre approche des soins aux patients. La synergie entre l\u2019expertise humaine et les prouesses informatiques de l\u2019IA promet d\u2019ouvrir de nouvelles perspectives sur l\u2019activit\u00e9 c\u00e9r\u00e9brale, ce qui permettra une interpr\u00e9tation plus pr\u00e9cise, des interventions plus pr\u00e9coces et de meilleurs r\u00e9sultats pour les patients.<a href=\"#_ftnref1\" name=\"_ftn1\"><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><!--HubSpot Call-to-Action Code --><span id=\"hs-cta-wrapper-a0cec4ab-373f-40cc-8133-bf16a3cf6595\" class=\"hs-cta-wrapper\"><span id=\"hs-cta-a0cec4ab-373f-40cc-8133-bf16a3cf6595\" class=\"hs-cta-node hs-cta-a0cec4ab-373f-40cc-8133-bf16a3cf6595\"><!-- [if lte IE 8]>\n\n\n<div id=\"hs-cta-ie-element\"><\/div>\n\n\n<![endif]--><a href=\"https:\/\/cta-redirect.hubspot.com\/cta\/redirect\/3002890\/a0cec4ab-373f-40cc-8133-bf16a3cf6595\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" id=\"hs-cta-img-a0cec4ab-373f-40cc-8133-bf16a3cf6595\" class=\"hs-cta-img aligncenter\" style=\"border-width: 0px;\" src=\"https:\/\/no-cache.hubspot.com\/cta\/default\/3002890\/a0cec4ab-373f-40cc-8133-bf16a3cf6595.png\" alt=\"practical applications of artificial intelligence in EEG\" \/><\/a><\/span><\/span><\/p>\n<p><span id=\"hs-cta-wrapper-a0cec4ab-373f-40cc-8133-bf16a3cf6595\" class=\"hs-cta-wrapper\"><script charset=\"utf-8\" src=\"https:\/\/js.hscta.net\/cta\/current.js\"><\/script><script type=\"text\/javascript\"> hbspt.cta.load(3002890, 'a0cec4ab-373f-40cc-8133-bf16a3cf6595', {\"useNewLoader\":\"true\",\"region\":\"na1\"}); <\/script><\/span><!-- end HubSpot Call-to-Action Code --><\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p><span style=\"font-size: 12px;\"><strong><span style=\"color: #008b96;\">SOURCES<\/span><\/strong><\/span><\/p>\n<p><span style=\"font-size: 11px;\">1. \u201c Lee LIT, Kanthasamy S, Ayyalaraju RS, Ganatra R. The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine. BJR Open. 2019 Oct 16;1(1):20190037. doi: 10.1259\/bjro.20190037. PMID: 33178956; PMCID: PMC7592467.<br \/>\n2. Tveit J, Aurlien H, Plis S, et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol. 2023;80(8):805\u2013812. doi:10.1001\/jamaneurol.2023.1645<br \/>\n3. Henry, K.E., Kornfield, R., Sridharan, A. et al. Human\u2013machine teaming is key to AI adoption: clinicians\u2019 experiences with a deployed machine learning system. npj Digit. Med. 5, 97 (2022). https:\/\/doi.org\/10.1038\/s41746-022-00597-7<br \/>\n4. Grant AC, Abdel-Baki SG, Weedon J, Arnedo V, Chari G, Koziorynska E, Lushbough C, Maus D, McSween T, Mortati KA, Reznikov A, Omurtag A. EEG interpretation reliability and interpreter confidence: a large single-center study. Epilepsy Behav. 2014 Mar;32:102-7. doi: 10.1016\/j.yebeh.2014.01.011. Epub 2014 Feb 13. PMID: 24531133; PMCID: PMC3965251.<br \/>\n5. Russell S, Kumar A. Providing Care: Intrinsic Human-Machine Teams and Data. Entropy (Basel). 2022 Sep 27;24(10):1369. doi: 10.3390\/e24101369. PMID: 37420389; PMCID: PMC9601264.<br \/>\n6. Tveit J, Aurlien H, Plis S, et al. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol. 2023;80(8):805\u2013812. doi:10.1001\/jamaneurol.2023.1645<br \/>\n7. Sahni, N R, G Stein, R Zemmel, and D M Cutler (2023), \u201cThe potential impact of artificial intelligence on healthcare spending\u201d, in A Agrawal, J Gans, A Goldfarb, and C Tucker (eds.), The Economics of Artificial Intelligence: Health Care Challenges.<br 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