Scientific pathophysiology of intrapartum fetal hypoxemia and cardiotocography pattern recognition-realignment is a basic prerequisite!

Improving the interpretation of electronic fetal monitoring: the fetal reserve index

Electronic fetal monitoring, particularly in the form of cardiotocography, forms the centerpiece of labor management. Initially successfully designed for stillbirth prevention, there was hope to also include prediction and prevention of fetal acidosis and its sequelae. With the routine use of electronic fetal monitoring, the cesarean delivery rate increased from <5% in the 1970s to >30% at present. Most at-risk cases produced healthy babies, resulting in part from considerable confusion as to the differences between diagnostic and screening tests. Electronic fetal monitoring is clearly a screening test. Multiple attempts have aimed at enhancing its ability to accurately distinguish babies at risk of in utero injury from those who are not and to do this in a timely manner so that appropriate intervention can be performed. Even key electronic fetal monitoring opinion leaders admit that this goal has yet to be achieved. Our group has developed a modified approach called the "Fetal Reserve Index" that contextualizes the findings of electronic fetal monitoring by formally including the presence of maternal, fetal, and obstetrical risk factors and increased uterine contraction frequencies and breaking up the tracing into 4 quantifiable components (heart rate, variability, decelerations, and accelerations). The result is a quantitative 8-point metric, with each variable being weighted equally in version 1.0. In multiple previously published refereed papers, we have shown that in head-to-head studies comparing the fetal reserve index with the American College of Obstetricians and Gynecologists' fetal heart rate categories, the fetal reserve index more accurately identifies babies born with cerebral palsy and could also reduce the rates of emergency cesarean delivery and vaginal operative deliveries. We found that the fetal reserve index scores and fetal pH and base excess actually begin to fall earlier in the first stage of labor than was commonly appreciated, and the fetal reserve index provides a good surrogate for pH and base excess values. Finally, the last fetal reserve index score before delivery combined with early analysis of neonatal heart rate and acid/base balance shows that the period of risk for neonatal neurologic impairment can continue for the first 30 minutes of life and requires much closer neonatal observation than is currently being done.

A deep feature fusion network for fetal state assessment

CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features' diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal's temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method's effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works.