{"id":13350,"date":"2024-07-09T18:26:52","date_gmt":"2024-07-09T18:26:52","guid":{"rendered":"https:\/\/dailyai.com\/?p=13350"},"modified":"2024-07-09T18:35:05","modified_gmt":"2024-07-09T18:35:05","slug":"ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"AI-gjennombrudd identifiserer raskt resistent tyfus uten eksponering for antibiotika"},"content":{"rendered":"

Forskere ved University of Cambridge har utnyttet kunstig intelligens i kampen mot antibiotikaresistens.\u00a0<\/strong><\/p>\n

Forskerteamet, ledet av professor Stephen Baker, utviklet et maskinl\u00e6ringsverkt\u00f8y som kun bruker mikroskopibilder for \u00e5 skille mellom bakterier som er resistente mot ciprofloksacin (et vanlig antibiotikum), og de som er mottakelige for det.<\/span><\/p>\n

Dette kan dramatisk redusere tiden det tar \u00e5 diagnostisere antibiotikaresistens, noe som potensielt kan endre hvordan vi behandler farlige infeksjoner som tyfoidfeber.<\/span><\/p>\n

Den <\/span>studie<\/span><\/a>publisert i Nature Communications, fokuserte p\u00e5 Salmonella Typhimurium, en bakterie som for\u00e5rsaker alvorlig gastrointestinal sykdom og kan f\u00f8re til livstruende invasiv sykdom.\u00a0<\/span><\/p>\n

\"AI\"
Salmonella er en bakterie som ofte smitter mennesker gjennom forurenset mat, og noen stammer har utviklet antibiotikaresistens. kilde: Universitetet i Cambridge<\/a>.<\/figcaption><\/figure>\n

Dr. Tuan-Anh Tran, en av n\u00f8kkelforskerne i prosjektet, forklarte fremgangsm\u00e5ten i en <\/span>blogginnlegg<\/span><\/a>: \"Det fine med maskinl\u00e6ringsmodellen er at den kan identifisere resistente bakterier basert p\u00e5 noen f\u00e5, subtile trekk p\u00e5 mikroskopibilder som det menneskelige \u00f8yet ikke kan oppdage.\"<\/span><\/p>\n

Forskningsprosessen har best\u00e5tt av flere viktige trinn:<\/span><\/p>\n

    \n
  1. Klargj\u00f8ring av bakteriepr\u00f8ver<\/strong>: Teamet dyrket S. Typhimurium-pr\u00f8ver i flytende n\u00e6ringsmedier, noen eksponert for ulike konsentrasjoner av ciprofloxacin og andre ikke.<\/span><\/li>\n
  2. Bildebehandling med h\u00f8yt innhold<\/strong>: Ved hjelp av et avansert mikroskop tok forskerne detaljerte bilder av bakteriene p\u00e5 flere tidspunkter.<\/span><\/li>\n
  3. Bildeanalyse<\/strong>: Spesialisert programvare hentet ut 65 forskjellige egenskaper fra hver bakteriecelle, inkludert form, st\u00f8rrelse og interaksjon med fluorescerende fargestoffer.<\/span><\/li>\n
  4. Utvikling av maskinl\u00e6ringsmodeller<\/strong>: Forskerne matet disse dataene inn i ulike maskinl\u00e6ringsalgoritmer og l\u00e6rte dem opp til \u00e5 gjenkjenne m\u00f8nstre forbundet med antibiotikaresistens.<\/span><\/li>\n
  5. Valg av funksjoner<\/strong>: Teamet identifiserte de mest avgj\u00f8rende egenskapene for \u00e5 skille mellom resistente og mottakelige bakterier.<\/span><\/li>\n<\/ol>\n

    Resultatene av denne prosessen var imponerende. AI-systemet identifiserte antibiotikaresistente bakterier korrekt i 87% av tilfellene.\u00a0<\/span><\/p>\n

    Det kanskje mest bemerkelsesverdige var at forskerne fant ut at resistente og mottakelige bakterier hadde tydelige visuelle m\u00f8nstre som AI-en kunne oppdage, selv n\u00e5r de ikke hadde blitt utsatt for antibiotika.\u00a0<\/span><\/p>\n

    Dette tyder p\u00e5 at antibiotikaresistens endrer bakterienes utseende p\u00e5 m\u00e5ter som er for subtile til at mennesker kan se dem, men som kunstig intelligens kan oppdage.<\/span><\/p>\n

    Dagens metoder krever vanligvis flere dager med bakteriedyrking og testing mot ulike antimikrobielle midler. Den nye AI-baserte metoden kan derimot potensielt gi resultater i l\u00f8pet av f\u00e5 timer.\u00a0<\/span><\/p>\n

    Raskere diagnose gj\u00f8r det mulig for legene \u00e5 forskrive de mest effektive antibiotikaene raskere, noe som potensielt kan gi bedre pasientresultater og redusere spredningen av resistente bakterier.<\/p>\n

    Fremover har forskerteamet som m\u00e5l \u00e5 utvide tiln\u00e6rmingen til mer komplekse kliniske pr\u00f8ver som blod eller urin, og teste dem p\u00e5 andre typer bakterier og antibiotika. De jobber ogs\u00e5 med \u00e5 gj\u00f8re teknologien mer tilgjengelig for sykehus og klinikker over hele verden.<\/span><\/p>\n

    Som professor Baker forklarer: \"Det som virkelig ville v\u00e6rt viktig, s\u00e6rlig i klinisk sammenheng, ville v\u00e6rt \u00e5 kunne ta en kompleks pr\u00f8ve - for eksempel blod, urin eller sputum - og identifisere mottakelighet og resistens direkte fra den.\"<\/p>\n

    \"Det er et mye mer komplisert problem, og det er egentlig ikke l\u00f8st i det hele tatt, selv ikke innen klinisk diagnostikk p\u00e5 sykehus. Hvis vi kunne finne en m\u00e5te \u00e5 gj\u00f8re dette p\u00e5, ville vi kunne redusere tiden det tar \u00e5 identifisere resistens, og det til en mye lavere kostnad. Det vil virkelig kunne v\u00e6re banebrytende.\"<\/span><\/p>\n

    Dr. Sushmita Sridhar oppsummerte virkningene slik: \"Siden denne tiln\u00e6rmingen bruker avbildning med enkeltcelleoppl\u00f8sning, er det enn\u00e5 ikke en l\u00f8sning som lett kan tas i bruk overalt. Men det er virkelig lovende at vi ved \u00e5 fange opp bare noen f\u00e5 parametere om bakterienes form og struktur kan f\u00e5 nok informasjon til \u00e5 forutsi legemiddelresistens p\u00e5 en relativt enkel m\u00e5te.\"<\/span><\/p>\n

    Antibiotikaresistens fortsetter \u00e5 utgj\u00f8re en eskalerende global helsetrussel, og innovative tiln\u00e6rminger som denne AI-drevne avbildningsteknikken gir nytt h\u00e5p.\u00a0<\/span><\/p>\n

    Dette er en del av en bredere trend med AI-drevne innovasjoner innen antibiotikaforskning. Ved MIT har forskere brukt dypl\u00e6ringsmodeller til \u00e5 oppdage en helt ny klasse av antibiotika<\/a>.<\/p>\n

    I mai i fjor kunngjorde et annet forskerteam at de hadde brukt kunstig intelligens til \u00e5 identifisere et nytt antibiotikum<\/a> effektive mot resistente bakterier.<\/p>\n

    Kunstig intelligens muliggj\u00f8r raskere og mer n\u00f8yaktig identifisering av legemiddelresistente infeksjoner, noe som baner vei for mer effektive behandlinger og bedre pasientresultater.\u00a0<\/span><\/p>\n

    De neste \u00e5rene vil bli avgj\u00f8rende n\u00e5r teamet skal omsette laboratoriesuksessen til kliniske anvendelser i den virkelige verden.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere ved University of Cambridge har utnyttet kunstig intelligens i kampen mot antibiotikaresistens. Forskerteamet, ledet av professor Stephen Baker, har utviklet et maskinl\u00e6ringsverkt\u00f8y som kun bruker mikroskopibilder til \u00e5 skille mellom bakterier som er resistente mot ciprofloxacin (et vanlig antibiotikum), og de som er mottakelige for det. Dette kan redusere tiden det tar \u00e5 diagnostisere antibiotikaresistens dramatisk, noe som potensielt kan endre hvordan vi behandler farlige infeksjoner som tyfoidfeber. Studien, som er publisert i Nature Communications, fokuserte p\u00e5 Salmonella Typhimurium, en bakterie som for\u00e5rsaker alvorlig gastrointestinal sykdom og kan f\u00f8re til livstruende invasiv sykdom. Dr. Tuan-Anh Tran, en av n\u00f8kkelforskerne i studien<\/p>","protected":false},"author":2,"featured_media":13352,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[353,105],"class_list":["post-13350","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biology","tag-machine-learning"],"yoast_head":"\nAI breakthrough rapidly identifies drug-resistant typhoid without antibiotic exposure | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI breakthrough rapidly identifies drug-resistant typhoid without antibiotic exposure | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Cambridge have harnessed AI in the fight against antibiotic resistance.\u00a0 The research team, led by Professor Stephen Baker, created a machine learning tool using only microscopy images to distinguish between bacteria resistant to ciprofloxacin (a common antibiotic) and those susceptible to it. This could dramatically reduce the time required for diagnosing antibiotic resistance, potentially transforming how we treat dangerous infections like typhoid fever. 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