{"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\/nl\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"AI-doorbraak identificeert snel resistente tyfus zonder blootstelling aan antibiotica"},"content":{"rendered":"

Onderzoekers van de Universiteit van Cambridge hebben AI ingezet in de strijd tegen antibioticaresistentie.\u00a0<\/strong><\/p>\n

Het onderzoeksteam, onder leiding van professor Stephen Baker, cre\u00eberde een tool voor machinaal leren die alleen microscopiebeelden gebruikt om onderscheid te maken tussen bacteri\u00ebn die resistent zijn tegen ciprofloxacine (een veelgebruikt antibioticum) en bacteri\u00ebn die er gevoelig voor zijn.<\/span><\/p>\n

Dit zou de tijd die nodig is voor het diagnosticeren van antibioticaresistentie drastisch kunnen verkorten, waardoor de manier waarop we gevaarlijke infecties zoals buiktyfus behandelen mogelijk verandert.<\/span><\/p>\n

De <\/span>onderzoek<\/span><\/a>gepubliceerd in Nature Communications, richtte zich op Salmonella Typhimurium, een bacterie die ernstige gastro-intestinale ziekten veroorzaakt en kan leiden tot levensbedreigende invasieve ziekten.\u00a0<\/span><\/p>\n

\"AI\"
Salmonella is een bacterie die mensen vaak besmet via besmet voedsel, en sommige stammen zijn resistent geworden tegen antibiotica. Bron: Universiteit van Cambridge<\/a>.<\/figcaption><\/figure>\n

Dr. Tuan-Anh Tran, een belangrijke onderzoeker van het project, legde de aanpak uit in een <\/span>blogpost<\/span><\/a>: \"Het mooie van het machine-learningmodel is dat het resistente bacteri\u00ebn kan identificeren op basis van een paar subtiele kenmerken op microscopiebeelden die menselijke ogen niet kunnen detecteren.\"<\/span><\/p>\n

Het onderzoeksproces omvatte verschillende belangrijke stappen:<\/span><\/p>\n

    \n
  1. Bacteri\u00eble monstervoorbereiding<\/strong>: Het team kweekte S. Typhimurium-monsters in vloeibare voedingsbodems, sommige blootgesteld aan verschillende concentraties ciprofloxacine en andere niet.<\/span><\/li>\n
  2. Beeldvorming met hoge inhoud<\/strong>: Met behulp van een geavanceerde microscoop maakten de onderzoekers gedetailleerde foto's van de bacteri\u00ebn op meerdere tijdstippen.<\/span><\/li>\n
  3. Beeldanalyse<\/strong>: Gespecialiseerde software extraheerde 65 verschillende kenmerken van elke bacteriecel, waaronder vorm, grootte en interactie met fluorescerende kleurstoffen.<\/span><\/li>\n
  4. Ontwikkeling van modellen voor machinaal leren<\/strong>: De onderzoekers voerden deze gegevens in verschillende algoritmen voor machinaal leren en trainden ze om patronen te herkennen die verband houden met antibioticaresistentie.<\/span><\/li>\n
  5. Eigenschapsselectie<\/strong>: Het team identificeerde de meest cruciale kenmerken voor het onderscheid tussen resistente en vatbare bacteri\u00ebn.<\/span><\/li>\n<\/ol>\n

    De resultaten van dit proces waren indrukwekkend. Het AI-systeem identificeerde antibioticaresistente bacteri\u00ebn in ongeveer 87% van de tijd correct.\u00a0<\/span><\/p>\n

    Het belangrijkste was misschien wel dat de onderzoekers ontdekten dat resistente en vatbare bacteri\u00ebn verschillende visuele patronen hadden die de AI kon detecteren, zelfs als ze niet waren blootgesteld aan antibiotica.\u00a0<\/span><\/p>\n

    Dit suggereert dat antibioticaresistentie het uiterlijk van bacteri\u00ebn verandert op manieren die te subtiel zijn voor mensen om te zien, maar die AI wel kan detecteren.<\/span><\/p>\n

    De huidige methoden vereisen doorgaans meerdere dagen van bacteriekweek en testen tegen verschillende antimicrobi\u00eble stoffen. De nieuwe AI-gebaseerde methode kan daarentegen mogelijk binnen enkele uren resultaten opleveren.\u00a0<\/span><\/p>\n

    Door een snellere diagnose kunnen artsen sneller de meest effectieve antibiotica voorschrijven, waardoor de resultaten voor de pati\u00ebnt mogelijk verbeteren en de verspreiding van resistente bacteri\u00ebn afneemt.<\/p>\n

    In de toekomst wil het onderzoeksteam hun aanpak uitbreiden naar complexere klinische monsters zoals bloed of urine en deze testen op andere soorten bacteri\u00ebn en antibiotica. Ze werken er ook aan om de technologie toegankelijker te maken voor ziekenhuizen en klinieken wereldwijd.<\/span><\/p>\n

    Zoals professor Baker uitlegt: \"Wat echt belangrijk zou zijn, vooral voor een klinische context, zou zijn om een complex monster te kunnen nemen - bijvoorbeeld bloed of urine of sputum - en de gevoeligheid en resistentie direct aan de hand daarvan te identificeren.\"<\/p>\n

    \"Dat is een veel ingewikkelder probleem dat nog helemaal niet is opgelost, zelfs niet in de klinische diagnostiek in een ziekenhuis. Als we een manier kunnen vinden om dit te doen, kunnen we de tijd die nodig is om resistentie tegen medicijnen vast te stellen verkorten en dat tegen veel lagere kosten. Dat zou echt transformerend kunnen zijn.\"<\/span><\/p>\n

    Dr. Sushmita Sridhar vatte de effecten als volgt samen: \"Aangezien deze benadering gebruik maakt van beeldvorming met een resolutie van \u00e9\u00e9n cel, is het nog geen oplossing die overal kan worden toegepast. Maar het laat echt zien dat het vastleggen van slechts een paar parameters over de vorm en structuur van de bacterie ons voldoende informatie kan geven om relatief eenvoudig resistentie tegen medicijnen te voorspellen.\"<\/span><\/p>\n

    Nu antibioticaresistentie een steeds grotere bedreiging blijft vormen voor de wereldgezondheid, bieden innovatieve benaderingen zoals deze AI-gestuurde beeldvormingstechniek nieuwe hoop.\u00a0<\/span><\/p>\n

    Dit maakt deel uit van een bredere trend van AI-gestuurde innovaties in antibioticaonderzoek. Op het MIT hebben onderzoekers deep learning-modellen gebruikt om een antibioticum te ontdekken. geheel nieuwe klasse antibiotica<\/a>.<\/p>\n

    In dezelfde geest kondigde een ander team wetenschappers in mei vorig jaar aan dat ze AI hadden gebruikt om een nieuw antibioticum identificeren<\/a> effectief tegen geneesmiddelresistente bacteri\u00ebn.<\/p>\n

    AI maakt een snellere en nauwkeurigere identificatie van geneesmiddelresistente infecties mogelijk, wat de weg vrijmaakt voor effectievere behandelingen en betere resultaten voor pati\u00ebnten.\u00a0<\/span><\/p>\n

    De komende jaren zullen cruciaal zijn voor het team om hun laboratoriumsuccessen te vertalen naar klinische toepassingen in de echte wereld.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Onderzoekers van de Universiteit van Cambridge hebben AI ingezet in de strijd tegen antibioticaresistentie. Het onderzoeksteam, onder leiding van professor Stephen Baker, cre\u00eberde een tool voor machinaal leren die alleen microscopiebeelden gebruikt om onderscheid te maken tussen bacteri\u00ebn die resistent zijn tegen ciprofloxacine (een veelgebruikt antibioticum) en bacteri\u00ebn die er gevoelig voor zijn. Dit zou de tijd die nodig is voor het diagnosticeren van antibioticaresistentie drastisch kunnen verkorten, waardoor we mogelijk gevaarlijke infecties zoals buiktyfus op een andere manier gaan behandelen. Het onderzoek, gepubliceerd in Nature Communications, richtte zich op Salmonella Typhimurium, een bacterie die ernstige gastro-intestinale ziekten veroorzaakt en kan leiden tot levensbedreigende invasieve ziekten. Dr. Tuan-Anh Tran, een belangrijke onderzoeker aan het<\/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\/nl\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\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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