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| Verfasst von: | Studier-Fischer, Alexander [VerfasserIn]  |
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| | Bressan, M. [VerfasserIn] |
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| | Qasim, A. bin [VerfasserIn] |
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| | Özdemir, B. [VerfasserIn] |
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| | Sellner, J. [VerfasserIn] |
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| | Seidlitz, S. [VerfasserIn] |
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| | Haney, C. M. [VerfasserIn] |
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| | Egen, Luisa [VerfasserIn] |
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| | Michel, Maurice Stephan [VerfasserIn] |
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| | Dietrich, Maximilian [VerfasserIn] |
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| | Salg, Gabriel Alexander [VerfasserIn] |
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| | Billmann, Franck [VerfasserIn] |
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| | Nienhüser, Henrik [VerfasserIn] |
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| | Hackert, Thilo [VerfasserIn] |
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| | Müller, Beat P. [VerfasserIn] |
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| | Maier-Hein, Lena [VerfasserIn] |
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| | Nickel, Felix [VerfasserIn] |
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| | Kowalewski, Karl-Friedrich [VerfasserIn] |
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| Titel: | Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence |
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| Verf.angabe: | A. Studier-Fischer, M. Bressan, A. bin Qasim, B. Özdemir, J. Sellner, S. Seidlitz, C. M. Haney, L. Egen, M. Michel, M. Dietrich, G. A. Salg, F. Billmann, H. Nienhüser, T. Hackert, B. P. Müller, L. Maier-Hein, F. Nickel & K. F. Kowalewski |
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| E-Jahr: | 2024 |
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| Jahr: | 27 July 2024 |
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| Umfang: | 18 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 05.02.2025 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Springer Nature, 2011 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 14(2024), Artikel-ID 17262, Seite 1-18 |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for “physiological” vs. 13.4% (± 3.1%) for “venous congestion”. A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit. |
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| DOI: | doi:10.1038/s41598-024-68280-3 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1038/s41598-024-68280-3 |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-024-68280-3 |
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| | DOI: https://doi.org/10.1038/s41598-024-68280-3 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Preclinical research |
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| | Translational research |
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| K10plus-PPN: | 1916386849 |
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| Verknüpfungen: | → Zeitschrift |
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Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence / Studier-Fischer, Alexander [VerfasserIn]; 27 July 2024 (Online-Ressource)
