Effect of Skin Pigmentation and Implantation Depth on Fluorescence-Based Oxygen Sensing in Ex Vivo Human Skin.

Abstract

BACKGROUND: Tissue oxygenation is crucial in reconstructive surgery, particularly when assessing flap viability. Current optical monitoring technologies, such as near-infrared spectroscopy (NIRS) and indocyanine green (ICG) dyes, encounter challenges due to variable signal strength influenced by skin pigmentation and tissue depth. Oxygen monitoring methods based on fluorescence lifetime could provide a more reliable and consistent alternative. METHODS: Benzoporphyrin-based oxygen sensors incorporated into polyHEMA hydrogel were assessed in ex vivo human skin representing all six Fitzpatrick skin types. We measured fluorescence intensity and lifetime at both intradermal and subcutaneous depths with controlled oxygen concentrations (100% and 12%). Additionally, sensor performance was tested across physiological ranges of temperature and pH to evaluate environmental robustness. RESULTS: We demonstrated that fluorescence lifetime measurements were consistent throughout all Fitzpatrick skin types at both intradermal and subcutaneous depths, while showing minimal sensitivity to pigmentation or implantation depth. In contrast, the absolute fluorescence intensity decreased with more skin pigmentation and demonstrated variability. Tissue oxygen tension (TOT) derived from lifetime measurements has significantly decreased under hypoxic conditions (12% oxygen) for all Fitzpatrick skin types, which confirms dependable oxygen responsiveness. Additionally, sensor performance remained consistent and stable within physiological temperature and pH ranges. CONCLUSIONS: Fluorescence lifetime sensing demonstrated stable and consistent performance across varying skin pigmentation and implantation depths under controlled ex vivo conditions. These findings support its potential as a pigmentation-independent sensing approach and provide a foundation for further in vivo validation. Future research in physiological and clinical models is needed to confirm performance under dynamic conditions.

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