Analysis of obstetrical deliveries under conduction anesthesia and immediate neonatal repercussion

Introduction: Anesthesia is an important resource for pain relief during labor. It is not a risk-free procedure and its use involves decision-making based on clinical and obstetric conditions, woman’s desire and availability of the procedure. This study aimed to analyze the association between this intervention and the occurrence of operative delivery and low Apgar score. Method: Retrospective study of a hospital database containing 5,282 parturients with single gestation of a fetus with cephalic presentation born alive and without malformation, among the 8,591 births that occurred from 2014 to 2017, in the Clinical Hospital’s Maternity of UFMG. Outcomes of interest were compared between deliveries conducted with or without anesthesia by association tests. Results: The occurrence of labor conduction anesthesia was 29.9%, being more frequent among adolescents (33.3% versus 29.1%; p = 0.008), nulliparous (39.7% versus 21.6%; p<0.001), those with induced delivery (40.6% versus 26.5%; p<0.001), patients with heart disease (53.5% versus 29.6%; p<0.001) and parturients whose babies weighed 2500 g or more at birth (31.3% versus 19.7%; p<0.001). There was an association between anesthesia and increased use of forceps (15.7% versus 1.8%; p<0.001) and vacuum extractor (2.0% versus 0.6%; p<0.001), however, there was a reduction in the occurrence of cesarean section (7.3% versus 12.9%; p<0.001). Anesthesia was associated with a higher occurrence of 1st minute Apgar <7 (p<0.001), but did not change the 5th Apgar score (p = 0.243). Nulliparity seems to influence the occurrence of cesarean delivery (8.6% versus 5.2%; p = 0.013) and forceps use (19.4% versus 9.8%; p<0.001). Conclusion: The use of labor conduction anesthesia was associated with operative vaginal delivery, the lowest cesarean section rate, with no impact on the 5th minute Apgar score.Introdução: A anestesia é um recurso importante no alívio da dor durante o trabalho de parto (TP). Não é um procedimento isento de riscos e sua utilização envolve decisão com base nas condições clínicas e obstétricas, desejo da mulher e disponibilidade do procedimento. O objetivo deste estudo foi analisar a associação entre essa intervenção com a ocorrência de parto operatório e baixo escore de Apgar. Método: Estudo retrospectivo de base de dados hospitalar contendo 5.282 parturientes com gestação única, de feto em apresentação cefálica nascido vivo e sem malformação, entre os 8.591 nascimentos ocorridos no período de 2014 a 2017, na maternidade do Hospital das Clínicas da UFMG. Desfechos de interesse foram comparados entre partos conduzidos com ou sem anestesia, através de testes de associação. Resultados: A ocorrência de anestesia de condução de TP foi de 29,9%, sendo mais frequente entre adolescentes (33,3% versus 29,1%; p = 0,008), nulíparas (39,7% versus 21,6%; p<0,001), naquelas com parto induzido (40,6% versus 26,5%; p<0,001), portadoras de cardiopatias (53,5% versus 29,6%; p<0,001) e parturientes cujos recém-nascidos pesaram 2500 g ou mais ao nascer (31,3% versus 19,7%; p<0,001). Houve associação entre anestesia e aumento do uso de fórceps (15,7% versus 1,8%; p<0,001) e de vacum extrator (2,0% versus 0,6%; p < 0,001), porém ocorreu redução das taxas de cesariana (7,3% versus 12,9%; p<0,001). O uso da anestesia associou-se à maior ocorrência de Apgar de 1o minuto < 7 (p<0,001), mas não alterou o de 5o (p=0,243). A nuliparidade parece ter influência sobre a ocorrência de parto cesariano (8,6% versus 5,2%; p = 0,013) e uso de fórceps (19,4% versus 9,8%; p<0.001). Conclusão: O uso de anestesia de condução no parto associou-se ao parto vaginal operatório, e à menor taxa de cesariana, sem impacto no Apgar de 5o minuto

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics