{"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\/da\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"AI-gennembrud identificerer hurtigt l\u00e6gemiddelresistent tyfus uden eksponering for antibiotika"},"content":{"rendered":"

Forskere ved University of Cambridge har udnyttet AI i kampen mod antibiotikaresistens.\u00a0<\/strong><\/p>\n

Forskerteamet, ledet af professor Stephen Baker, skabte et maskinl\u00e6ringsv\u00e6rkt\u00f8j, der kun bruger mikroskopibilleder til at skelne mellem bakterier, der er resistente over for ciprofloxacin (et almindeligt antibiotikum), og dem, der er modtagelige over for det.<\/span><\/p>\n

Det kan dramatisk reducere den tid, det tager at diagnosticere antibiotikaresistens, og potentielt \u00e6ndre den m\u00e5de, vi behandler farlige infektioner som tyfus p\u00e5.<\/span><\/p>\n

Den <\/span>unders\u00f8gelse<\/span><\/a>som blev offentliggjort i Nature Communications, fokuserede p\u00e5 Salmonella Typhimurium, en bakterie, der for\u00e5rsager alvorlig mave-tarm-sygdom og kan f\u00f8re til livstruende invasiv sygdom.\u00a0<\/span><\/p>\n

\"AI\"
Salmonella er en bakterie, der ofte inficerer mennesker gennem forurenede f\u00f8devarer, og nogle stammer er blevet resistente over for antibiotika. Kilde: Universitetet i Cambridge<\/a>.<\/figcaption><\/figure>\n

Dr. Tuan-Anh Tran, en af projektets n\u00f8gleforskere, forklarede tilgangen i en <\/span>blogindl\u00e6g<\/span><\/a>: \"Det smukke ved maskinl\u00e6ringsmodellen er, at den kan identificere resistente bakterier ud fra nogle f\u00e5 subtile tr\u00e6k p\u00e5 mikroskopibilleder, som det menneskelige \u00f8je ikke kan opdage.\"<\/span><\/p>\n

Forskningsprocessen omfattede flere vigtige trin:<\/span><\/p>\n

    \n
  1. Forberedelse af bakteriepr\u00f8ver<\/strong>: Holdet dyrkede S. Typhimurium-pr\u00f8ver i flydende n\u00e6ringsmedier, hvor nogle blev udsat for forskellige koncentrationer af ciprofloxacin og andre ikke.<\/span><\/li>\n
  2. Billeddannelse med h\u00f8jt indhold<\/strong>: Ved hj\u00e6lp af et sofistikeret mikroskop tog forskerne detaljerede billeder af bakterierne p\u00e5 flere tidspunkter.<\/span><\/li>\n
  3. Billedanalyse<\/strong>: Specialiseret software udtrak 65 forskellige funktioner fra hver bakteriecelle, herunder form, st\u00f8rrelse og interaktion med fluorescerende farvestoffer.<\/span><\/li>\n
  4. Udvikling af maskinl\u00e6ringsmodeller<\/strong>: Forskerne f\u00f8rte disse data ind i forskellige maskinl\u00e6ringsalgoritmer og tr\u00e6nede dem i at genkende m\u00f8nstre, der er forbundet med antibiotikaresistens.<\/span><\/li>\n
  5. Udv\u00e6lgelse af funktioner<\/strong>: Holdet identificerede de mest afg\u00f8rende tr\u00e6k til at skelne mellem resistente og modtagelige bakterier.<\/span><\/li>\n<\/ol>\n

    Resultaterne af denne proces var imponerende. AI-systemet identificerede antibiotikaresistente bakterier korrekt i ca. 87% af tilf\u00e6ldene.\u00a0<\/span><\/p>\n

    M\u00e5ske er det mest bem\u00e6rkelsesv\u00e6rdige, at forskerne fandt ud af, at resistente og modtagelige bakterier havde forskellige visuelle m\u00f8nstre, som AI'en kunne registrere, selv n\u00e5r de ikke havde v\u00e6ret udsat for antibiotika.\u00a0<\/span><\/p>\n

    Det tyder p\u00e5, at antibiotikaresistens \u00e6ndrer bakteriernes udseende p\u00e5 m\u00e5der, der er for subtile til, at mennesker kan se dem, men som kunstig intelligens kan opdage.<\/span><\/p>\n

    De nuv\u00e6rende metoder kr\u00e6ver typisk flere dages bakteriekultur og test mod forskellige antimikrobielle stoffer. I mods\u00e6tning hertil kan den nye AI-baserede metode potentielt give resultater inden for f\u00e5 timer.\u00a0<\/span><\/p>\n

    Hurtigere diagnose g\u00f8r det muligt for l\u00e6gerne at ordinere de mest effektive antibiotika hurtigere, hvilket potentielt kan forbedre patientresultaterne og reducere spredningen af resistente bakterier.<\/p>\n

    Fremover vil forskerholdet udvide deres tilgang til mere komplekse kliniske pr\u00f8ver som blod eller urin og teste dem p\u00e5 andre typer bakterier og antibiotika. De arbejder ogs\u00e5 p\u00e5 at g\u00f8re teknologien mere tilg\u00e6ngelig for hospitaler og klinikker verden over.<\/span><\/p>\n

    Som professor Baker forklarer: \"Det, der ville v\u00e6re virkelig vigtigt, is\u00e6r i en klinisk sammenh\u00e6ng, ville v\u00e6re at kunne tage en kompleks pr\u00f8ve - for eksempel blod, urin eller sputum - og identificere modtagelighed og resistens direkte ud fra den.\"<\/p>\n

    \"Det er et meget mere kompliceret problem, og det er slet ikke blevet l\u00f8st, ikke engang inden for klinisk diagnostik p\u00e5 et hospital. Hvis vi kunne finde en m\u00e5de at g\u00f8re det p\u00e5, kunne vi reducere den tid, det tager at identificere l\u00e6gemiddelresistens, og til en meget lavere pris. Det kunne virkelig v\u00e6re transformerende.\"<\/span><\/p>\n

    Dr. Sushmita Sridhar opsummerede virkningerne og sagde: \"I betragtning af at denne tilgang bruger billeddannelse med enkeltcelleopl\u00f8sning, er det endnu ikke en l\u00f8sning, der let kan implementeres overalt. Men det er virkelig lovende, at vi ved at indfange blot nogle f\u00e5 parametre om bakteriernes form og struktur kan give os nok information til relativt nemt at forudsige l\u00e6gemiddelresistens.\"<\/span><\/p>\n

    Da antibiotikaresistens fortsat udg\u00f8r en eskalerende global sundhedstrussel, giver innovative tilgange som denne AI-drevne billeddannelsesteknik nyt h\u00e5b.\u00a0<\/span><\/p>\n

    Dette er en del af en bredere tendens med AI-drevne innovationer inden for antibiotikaforskning. P\u00e5 MIT har forskere brugt deep learning-modeller til at opdage en Helt ny klasse af antibiotika<\/a>.<\/p>\n

    P\u00e5 samme m\u00e5de meddelte et andet hold forskere i maj sidste \u00e5r, at de havde brugt AI til at identificere et nyt antibiotikum<\/a> effektiv mod resistente bakterier.<\/p>\n

    AI muligg\u00f8r hurtigere og mere pr\u00e6cis identifikation af l\u00e6gemiddelresistente infektioner, hvilket baner vejen for mere effektive behandlinger og bedre patientresultater.\u00a0<\/span><\/p>\n

    De n\u00e6ste par \u00e5r bliver afg\u00f8rende, da teamet arbejder p\u00e5 at oms\u00e6tte deres laboratoriesucces til kliniske anvendelser i den virkelige verden.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere ved University of Cambridge har udnyttet kunstig intelligens i kampen mod antibiotikaresistens. Forskergruppen, der ledes af professor Stephen Baker, har skabt et maskinl\u00e6ringsv\u00e6rkt\u00f8j, der kun bruger mikroskopibilleder til at skelne mellem bakterier, der er resistente over for ciprofloxacin (et almindeligt antibiotikum), og dem, der er modtagelige over for det. Det kan dramatisk reducere den tid, det tager at diagnosticere antibiotikaresistens, og potentielt \u00e6ndre den m\u00e5de, vi behandler farlige infektioner som tyfus p\u00e5. Unders\u00f8gelsen, der blev offentliggjort i Nature Communications, fokuserede p\u00e5 Salmonella Typhimurium, en bakterie, der for\u00e5rsager alvorlig mave-tarm-sygdom og kan f\u00f8re til livstruende invasiv sygdom. Dr. Tuan-Anh Tran, en af n\u00f8gleforskerne i unders\u00f8gelsen<\/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\/da\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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