Ph.D. Thesis Defense: Photonic technology for diagnosis of perinatal asphyxia

CIC nanoGUNE Seminars

Speaker

Ion Olaetxea, Nanoengineering Group, nanoGUNE

When

2022/06/07
13:00

Place

CFM Auditorium

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**Photonic technology for diagnosis of perinatal asphyxia** Ion Olaetxea _Nanoengineering Group, CIC nanoGUNE_ 
Worldwide, the number of newborn deaths counts for 2.5 million, of which 24% are caused by intrapartum complications such as perinatal asphyxia, one of the most common medical disorders in high-risk births. Characterized by insufficient oxygen supply to fetal tissue, a prolonged episode of perinatal asphyxia is responsible for neurological damage known as hypoxic-ischemic encephalopathy. Fetal monitoring plays a major role in the diagnosis of perinatal asphyxia, and consequent neonatal adverse outcomes. However, performance of current standard methods, such as Cardiotocography (CTG) or Fetal Scalp Blood Sampling (FSBS) have been questioned in obstetric care. While CTG has been related with an increase of cesarean sections and instrumental deliveries, pH, or alternatively lactate, obtained from FSBS have demonstrated limited evidence for reduction of cesarean deliveries and adverse neonatal outcome. The method, which operates in an invasive, non continuous and intermittent fashion, does not provide a real picture of the clinical status of the fetus, leading to a high misclassification rate. The primary goal of this thesis is the development of a non-invasive clinical tool for continuous and real-time monitoring of asphyxia during delivery. The new technology combines Raman spectroscopy, which is a highly specific vibrational spectroscopy method, with machine learning algorithms. Equipped with an application-specific probe, the technology takes into account the systemic picture of physiological variations or anomalies, compared to state- of-the-art, where a single parameter, as pH or lactate, serves as base for decision-making. This results in a much more sensitive and stable identification of pathological states and prediction of specific parameters that will support in an innovative way immediate medical decision-making. ** ** ** ** ** ** **Supervisor:** Andreas Seifert and Joseba Zubia (EHU)