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Castelblanco, A. ; Ruggeri, E.* ; Matzeu, G.* ; Heydarian, M. ; Förster, K. ; Bahnasy, A.* ; Flemmer, A.* ; Schnabel, J.A. ; Schubert, B. ; Omenetto, F.G.* ; Hilgendorff, A.

Artificial intelligence-supported colorimetric multibiomarker sensor to enable critical neonatal monitoring.

ACS sens., DOI: 10.1021/acssensors.5c04171 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Clinical monitoring in the most vulnerable patients, such as newborns, relies on invasive and costly procedures and/or wired sensor surveillance, increasing discomfort and risk for undetected events. Addressing this critical need, we present a noninvasive, AI-supported multibiomarker monitoring sensor capturing multiple critical body functions via colorimetric analysis of body fluids. The sensor is optimized for its use in critically ill preterm neonates, where transepidermal fluid mirrors the interstitial compartment, thereby opening new avenues for future clinical monitoring. The silk-based sensor introduces twelve different colorimetric inks that stabilize labile bioresponsive molecules to a simple, wax-printed paper microfluidic wearable patch to facilitate real-time tracking of biomarkers (temperature (T), pH, sodium (S), and glucose (G)) on a miniaturized surface. Colorimetric responses showed high precision and reproducibility in sensing ranges relevant for neonatal care (T: 32-41 [°C]; pH: 3-9; S: 2.92-29.20 [mg/mL], G: 0.039-0.625 [mg/mL]). Deep learning for color response quantification achieved high-precision estimates (T: 0.455 [°C]; pH: 0.416; S: 0.857 [mg/mL], G:0.019 [mg/mL]; mean absolute error). The sensor's optimized interface for sample collection on skin and its performance under clinically relevant conditions, e.g., increased humidity (80%), tracking on moving object (0.986 AP@IoU = 0.5), sensor shear/rotation, uncontrolled light, as well as successful capture of physiological effects in biological fluids together with its miniaturized design caters to the critical needs of intensified monitoring in high-end patient populations such as neonates.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Ai Colorimetric Readout ; Biofluid ; Colorimetric Sensors ; Computer Vision Newborns ; Neonatal Incubator ; Newborn Biomarker ; Noninvasive Monitoring ; Transepidermal Fluid ; Wearable Skin Sensors; Electrolyte Management; Cystic-fibrosis; Fluid
ISSN (print) / ISBN 2379-3694
e-ISSN 2379-3694
Zeitschrift ACS sensors
Verlag American Chemical Society (ACS)
Verlagsort Washington, DC
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Lung Health and Immunity (LHI)
Institute of Computational Biology (ICB)
Institute for Machine Learning in Biomed Imaging (IML)
Förderungen Stiftung AtemWeg (LSS AIRR)
BMBF
Helmholtz Association under the research school Munich School for Data Science
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Centre for Lung Research (DZL) (BMBF)
Federal Ministry of Education and Research in Germany (BMBF), Helmholtz Munich, Germany
Young Investigator Grant NWG VH-NG-829 (Helmholtz Association)