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Interpretation magnetresonanz-tomographischer (MRT) Daten mit KI

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Künstliche Intelligenz im Gesundheitswesen

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Zusammenfassung

Die rasante Entwicklung der Magnetresonanz-Tomografie sowie Fortschritte in der Verfügbarkeit leistungsfähiger Rechentechnik haben in den letzten Jahren neue Perspektiven für die Nutzung radiologischer Bilddaten als Biomarker eröffnet. Dadurch sind bildgestützte Verfahren möglich geworden, die Aussagen über den Krankheitsverlauf und die Wirkung verschiedener Therapieformen erlauben (Radiomics). Ausgehend von klassischen Methoden der Mustererkennung werden die Grundprinzipien und Einsatzmöglichkeiten der KI-basierten Bildinterpretation von MRT-Daten erläutert. Hierzu gehören einfache und fortgeschrittene Klassifikatoren, künstliche neuronale Netze, Convolutional Neural Networks sowie deren Verwendung für Radiomics Anwendungen.

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  1. 1.

    (MRI[Title/Abstract]) AND ((artificial intelligence[Title/Abstract]) OR (pattern recognition[Title/Abstract]) OR (artificial neural network[Title/Abstract]) OR (machine learning[Title/Abstract]) OR (computer vision[Title/Abstract]))

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  1. Fakultät für Elektrotechnik und Informatik, Hochschule Stralsund, Stralsund, Deutschland

    Hans-Heino Ehricke

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  1. Hans-Heino Ehricke
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Correspondence to Hans-Heino Ehricke .

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  1. Hochschule für angewandte Wissenschaften Neu-Ulm, Fakultät Gesundheitsmanagement, Neu-Ulm, Deutschland

    Mario A. Pfannstiel

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Ehricke, HH. (2022). Interpretation magnetresonanz-tomographischer (MRT) Daten mit KI. In: Pfannstiel, M.A. (eds) Künstliche Intelligenz im Gesundheitswesen. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-33597-7_30

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  • DOI: https://doi.org/10.1007/978-3-658-33597-7_30

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