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AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients: health and medicine

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcirculatory alterations, we propose a deep learning app...

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Hauptverfasser: Seidlitz, Silvia (VerfasserIn) , Hölzl, Katharina (VerfasserIn) , Garrel, Ayca von (VerfasserIn) , Sellner, Jan (VerfasserIn) , Katzenschlager, Stephan (VerfasserIn) , Hölle, Tobias (VerfasserIn) , Fischer, Dania (VerfasserIn) , Forst, Maik von der (VerfasserIn) , Schmitt, Felix (VerfasserIn) , Studier-Fischer, Alexander (VerfasserIn) , Weigand, Markus A. (VerfasserIn) , Maier-Hein, Lena (VerfasserIn) , Dietrich, Maximilian (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 18 July 2025
In: Science advances
Year: 2025, Jahrgang: 11, Heft: 29, Pages: 1-12
ISSN:2375-2548
DOI:10.1126/sciadv.adw1968
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1126/sciadv.adw1968
Verlag, kostenfrei, Volltext: http://www.science.org/doi/10.1126/sciadv.adw1968
Volltext
Verfasserangaben:Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel, Jan Sellner, Stephan Katzenschlager, Tobias Hölle, Dania Fischer, Maik von der Forst, Felix C.F. Schmitt, Alexander Studier-Fischer, Markus A. Weigand, Lena Maier-Hein, Maximilian Dietrich
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Zusammenfassung:With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcirculatory alterations, we propose a deep learning approach to automated sepsis diagnosis and mortality prediction using a single HSI cube acquired within seconds. In a prospective observational study, we collected HSI data from the palms and fingers of more than 480 intensive care unit patients. Neural networks applied to HSI measurements predicted sepsis and mortality with areas under the receiver operating characteristic curve (AUROCs) of 0.80 and 0.72, respectively. Performance improved substantially with additional clinical data, reaching AUROCs of 0.94 for sepsis and 0.83 for mortality. We conclude that deep learning-based HSI analysis enables rapid and noninvasive prediction of sepsis and mortality, with a potential clinical value for enhancing diagnosis and treatment.
Beschreibung:Gesehen am 22.09.2025
Beschreibung:Online Resource
ISSN:2375-2548
DOI:10.1126/sciadv.adw1968

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