Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Hyperspectral imaging for monitoring oxygen saturation levels during normothermic kidney perfusion

The development of improved preservation techniques and the reliable assessment of donor grafts are main fields of research in transplantation medicine. Normothermic machine perfusion (NMP) is a promising alternative to static cold storage of organs, maintaining physiological conditions during preservation. In combination with NMP, we introduce hyperspectral imaging (HSI) as a novel approach for the monitoring of physiological kidney parameters. A line-scan HSI camera system was used to record images of porcine kidneys during NMP. Based on a dual-wavelength algorithm, the oxygen saturation levels were calculated from HSI recordings. Furthermore, we observed HSI images in the near-infrared (NIR) range in order to detect water characteristics of the kidney tissue. We found increasing levels of oxygenation during NMP and could discriminate between perfused and non-perfused areas. Cysts at the renal capsula were characterized by an absorption increase in the NIR band. Within this work, we showed that HSI is able to detect relevant chemical changes during NMP and allows the identification of pathologic variations

A handheld fiber-optic probe to enable optical coherence tomography of oral soft tissue

This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherence tomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue disease such as oral lichen planus. However, usage of OCT in the oral cavity has been limited, as the requirements for high-quality optical scanning have often resulted in probes that are heavy, unwieldy and clinically impractical. In this paper, we present a novel probe that combines an all-fiber optical design with a light-weight magnetic scanning mechanism to provide easy access to the oral cavity. The resulting probe is approximately the size of a pen (10 mm × 140 mm) and weighs only 10 grams. To demonstrate the feasibility and high image quality achieved with the probe, imaging was performed on the buccal mucosa and alveolar mucosa during routine clinical assessment of six patients diagnosed with oral lichen planus. Results show the loss of normal tissue structure within the lesion, and contrast this with the clear delineation of tissue layers in adjacent inconspicuous regions. The results also demonstrate the ability of the probe to acquire a three-dimensional data volume by manually sweeping across the surface of the mucosa. The findings of this study show the feasibility of using a small, lightweight probe to identify pathological features in oral soft tissue.Julia Walther, Jonas Golde, Marius Albrecht, Bryden C. Quirk, Loretta Scolaro, Rodney W. Kirk, Yuliia Gruda, Christian Schnabel, Florian Tetschke, Korinna Joehrens, Dominik Haim, Michaela Buckova, Jiawen Li, and Robert A. McLaughli