In Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Surveillance of physiological parameters of newborns during delivery triggers medical decision-making, can rescue life and health, and helps avoid unnecessary cesareans. Here, the development of a photonic technology for monitoring perinatal asphyxia is presented and validated in vivo in a preclinical stage. Contrary to state of the art, the technology provides continuous data in real-time in a non-invasive manner. Moreover, the technology does not rely on a single parameter as pH or lactate, instead monitors changes of the entirety of physiological parameters accessible by Raman spectroscopy. By a fiber-coupled Raman probe that is in controlled contact with the skin of the subject, near-infrared Raman spectra are measured and analyzed by machine learning algorithms to develop classification models. As a performance benchmarking, various hybrid and non-hybrid classifiers are tested. In an asphyxia model in newborn pigs, more than 1000 Raman spectra are acquired at three different clinical phases-basal condition, hypoxia-ischemia, and post-hypoxia-ischemia stage. In this preclinical proof-of-concept study, figures of merit reach 90% levels for classifying the clinical phases and demonstrate the power of the technology as an innovative medical tool for diagnosing a perinatal adverse outcome.
Olaetxea Ion, Lafuente Hector, Lopez Eneko, Izeta Ander, Jaunarena Ibon, Seifert Andreas
2022-Nov-15
Raman spectroscopy, hypoxia-ischemia, machine learning, perinatal asphyxia, photonic monitoring
