{"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\/de\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"KI-Durchbruch identifiziert schnell medikamentenresistenten Typhus ohne Antibiotikaexposition"},"content":{"rendered":"

Forscher der Universit\u00e4t Cambridge haben KI f\u00fcr den Kampf gegen Antibiotikaresistenzen nutzbar gemacht.\u00a0<\/strong><\/p>\n

Das Forscherteam unter der Leitung von Professor Stephen Baker hat ein maschinelles Lernprogramm entwickelt, das nur Mikroskopbilder verwendet, um zwischen Bakterien, die gegen Ciprofloxacin (ein g\u00e4ngiges Antibiotikum) resistent sind, und solchen, die daf\u00fcr empf\u00e4nglich sind, zu unterscheiden.<\/span><\/p>\n

Dies k\u00f6nnte die Zeit, die f\u00fcr die Diagnose von Antibiotikaresistenzen ben\u00f6tigt wird, drastisch verk\u00fcrzen und die Behandlung gef\u00e4hrlicher Infektionen wie Typhus m\u00f6glicherweise ver\u00e4ndern.<\/span><\/p>\n

Die <\/span>Studie<\/span><\/a>Die in Nature Communications ver\u00f6ffentlichte Studie befasste sich mit Salmonella Typhimurium, einem Bakterium, das schwere Magen-Darm-Erkrankungen verursacht und zu lebensbedrohlichen invasiven Erkrankungen f\u00fchren kann.\u00a0<\/span><\/p>\n

\"AI\"
Salmonellen sind Bakterien, die den Menschen h\u00e4ufig \u00fcber verunreinigte Lebensmittel infizieren, und einige St\u00e4mme haben eine Antibiotikaresistenz entwickelt. Quelle: Universit\u00e4t von Cambridge<\/a>.<\/figcaption><\/figure>\n

Dr. Tuan-Anh Tran, eine der wichtigsten Forscherinnen des Projekts, erl\u00e4uterte den Ansatz in einem <\/span>Blog-Beitrag<\/span><\/a>: \"Das Sch\u00f6ne an dem Modell des maschinellen Lernens ist, dass es resistente Bakterien anhand einiger subtiler Merkmale auf Mikroskopbildern identifizieren kann, die das menschliche Auge nicht erkennen kann.\"<\/span><\/p>\n

Der Forschungsprozess umfasste mehrere wichtige Schritte:<\/span><\/p>\n

    \n
  1. Vorbereitung der bakteriellen Proben<\/strong>: Das Team z\u00fcchtete Proben von S. Typhimurium in fl\u00fcssigen N\u00e4hrmedien, von denen einige unterschiedlichen Konzentrationen von Ciprofloxacin ausgesetzt waren und andere nicht.<\/span><\/li>\n
  2. Hochaufl\u00f6sende Bildgebung<\/strong>: Mit Hilfe eines hochentwickelten Mikroskops machten die Forscher detaillierte Aufnahmen der Bakterien zu verschiedenen Zeitpunkten.<\/span><\/li>\n
  3. Bildanalyse<\/strong>: Eine spezielle Software extrahierte 65 verschiedene Merkmale aus jeder Bakterienzelle, darunter Form, Gr\u00f6\u00dfe und Interaktion mit Fluoreszenzfarbstoffen.<\/span><\/li>\n
  4. Entwicklung von Modellen f\u00fcr maschinelles Lernen<\/strong>: Die Forscher speisten diese Daten in verschiedene Algorithmen f\u00fcr maschinelles Lernen ein und trainierten sie darauf, Muster zu erkennen, die mit Antibiotikaresistenzen in Verbindung stehen.<\/span><\/li>\n
  5. Auswahl der Merkmale<\/strong>: Das Team hat die wichtigsten Merkmale zur Unterscheidung zwischen resistenten und anf\u00e4lligen Bakterien ermittelt.<\/span><\/li>\n<\/ol>\n

    Die Ergebnisse dieses Prozesses waren beeindruckend. Das KI-System identifizierte antibiotikaresistente Bakterien in etwa 87% der F\u00e4lle richtig.\u00a0<\/span><\/p>\n

    Am wichtigsten ist vielleicht, dass die Forscher feststellten, dass resistente und anf\u00e4llige Bakterien unterschiedliche visuelle Muster aufwiesen, die die KI erkennen konnte, selbst wenn sie nicht mit Antibiotika behandelt worden waren.\u00a0<\/span><\/p>\n

    Dies deutet darauf hin, dass die Antibiotikaresistenz das Erscheinungsbild von Bakterien auf eine Weise ver\u00e4ndert, die f\u00fcr den Menschen zu subtil ist, von der KI aber erkannt werden kann.<\/span><\/p>\n

    Derzeitige Methoden erfordern in der Regel eine mehrt\u00e4gige Bakterienkultur und Tests mit verschiedenen antimikrobiellen Mitteln. Im Gegensatz dazu k\u00f6nnte die neue KI-basierte Methode m\u00f6glicherweise innerhalb von Stunden Ergebnisse liefern.\u00a0<\/span><\/p>\n

    Eine schnellere Diagnose erm\u00f6glicht es den \u00c4rzten, die wirksamsten Antibiotika fr\u00fcher zu verschreiben, wodurch sich die Ergebnisse f\u00fcr die Patienten verbessern und die Verbreitung resistenter Bakterien verringert werden kann.<\/p>\n

    In Zukunft will das Forschungsteam seinen Ansatz auf komplexere klinische Proben wie Blut oder Urin ausweiten und sie auf andere Bakterienarten und Antibiotika testen. Au\u00dferdem arbeiten sie daran, die Technologie f\u00fcr Krankenh\u00e4user und Kliniken auf der ganzen Welt besser zug\u00e4nglich zu machen.<\/span><\/p>\n

    Wie Professor Baker erkl\u00e4rt: \"Was wirklich wichtig w\u00e4re, vor allem f\u00fcr den klinischen Kontext, w\u00e4re die M\u00f6glichkeit, eine komplexe Probe zu nehmen - zum Beispiel Blut, Urin oder Sputum - und die Anf\u00e4lligkeit und Resistenz direkt daraus zu bestimmen.\"<\/p>\n

    \"Das ist ein viel komplizierteres Problem, das selbst in der klinischen Diagnostik in einem Krankenhaus noch nicht wirklich gel\u00f6st ist. Wenn wir einen Weg finden w\u00fcrden, dies zu tun, k\u00f6nnten wir die Zeit, die f\u00fcr die Identifizierung von Arzneimittelresistenzen ben\u00f6tigt wird, verk\u00fcrzen, und das zu wesentlich geringeren Kosten. Das k\u00f6nnte wirklich transformativ sein.\"<\/span><\/p>\n

    Dr. Sushmita Sridhar fasste die Auswirkungen zusammen und erkl\u00e4rte: \"Da bei diesem Ansatz die Bildgebung mit Einzelzellaufl\u00f6sung zum Einsatz kommt, ist es noch keine L\u00f6sung, die ohne weiteres \u00fcberall eingesetzt werden kann. Aber es ist vielversprechend, dass wir durch die Erfassung einiger weniger Parameter \u00fcber die Form und Struktur der Bakterien gen\u00fcgend Informationen erhalten, um relativ einfach eine Vorhersage \u00fcber die Arzneimittelresistenz treffen zu k\u00f6nnen.\"<\/span><\/p>\n

    Da die Antibiotikaresistenz weiterhin eine zunehmende globale Gesundheitsbedrohung darstellt, bieten innovative Ans\u00e4tze wie diese KI-gest\u00fctzte Bildgebungstechnik neue Hoffnung.\u00a0<\/span><\/p>\n

    Dies ist Teil eines breiteren Trends von KI-gesteuerten Innovationen in der Antibiotikaforschung. Am MIT haben Forscher mithilfe von Deep-Learning-Modellen ein v\u00f6llig neue Klasse von Antibiotika<\/a>.<\/p>\n

    In \u00e4hnlicher Weise gab ein anderes Team von Wissenschaftlern im Mai letzten Jahres bekannt, dass sie KI eingesetzt haben, um ein neues Antibiotikum zu identifizieren<\/a> wirksam gegen arzneimittelresistente Bakterien.<\/p>\n

    KI erm\u00f6glicht eine schnellere und genauere Identifizierung von arzneimittelresistenten Infektionen und ebnet so den Weg f\u00fcr wirksamere Behandlungen und bessere Patientenergebnisse.\u00a0<\/span><\/p>\n

    Die n\u00e4chsten Jahre werden entscheidend sein, da das Team daran arbeitet, seinen Erfolg im Labor in reale klinische Anwendungen umzusetzen.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forscher der Universit\u00e4t Cambridge haben KI f\u00fcr den Kampf gegen Antibiotikaresistenzen nutzbar gemacht. Das Forscherteam unter der Leitung von Professor Stephen Baker hat ein maschinelles Lernwerkzeug entwickelt, das nur Mikroskopbilder verwendet, um zwischen Bakterien, die gegen Ciprofloxacin (ein g\u00e4ngiges Antibiotikum) resistent sind, und solchen, die daf\u00fcr empf\u00e4nglich sind, zu unterscheiden. Dies k\u00f6nnte die f\u00fcr die Diagnose von Antibiotikaresistenzen ben\u00f6tigte Zeit drastisch verk\u00fcrzen und die Behandlung gef\u00e4hrlicher Infektionen wie Typhus m\u00f6glicherweise ver\u00e4ndern. Die Studie, die in Nature Communications ver\u00f6ffentlicht wurde, konzentrierte sich auf Salmonella Typhimurium, ein Bakterium, das schwere Magen-Darm-Erkrankungen verursacht und zu lebensbedrohlichen invasiven Erkrankungen f\u00fchren kann. Dr. Tuan-Anh Tran, eine der Hauptforscherinnen der<\/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\/de\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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