Ontologies, Machine Learning and Deep Learning in Obstetrics

Malgieri, Lorenzo E.

doi:10.1007/978-3-031-10067-3_3

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…A multi-physics approach to represent the complex biochemo–electro–mechanical model to predict the interaction between biochemical, electrical, and mechanical fields during uterine contractions, normal functioning, and arrest, and further validation studies and implementation research will be needed to monitor the structural and functional changes in labor outcomes. Deep Learning, considered by all to be a subset of machine learning [ 85 ], and graph neural networks (GNNs) can be used to build a virtual electromyometrical model using partial derivatives as a computational approach with a space-time interconnection.…”

Section: Discussionmentioning

confidence: 99%

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Malvasi,

Baldini,

Cicinelli

et al. 2024

IJMS

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In eutocic labor, the autonomic nervous system is dominated by the parasympathetic system, which ensures optimal blood flow to the uterus and placenta. This study is focused on the detection of the quantitative presence of catecholamine (C) neurofibers in the internal uterine orifice (IUO) and in the lower uterine segment (LUS) of the pregnant uterus, which could play a role in labor and delivery. A total of 102 women were enrolled before their submission to a scheduled cesarean section (CS); patients showed a singleton fetus in a cephalic presentation outside labor. During CS, surgeons sampled two serial consecutive full-thickness sections 5 mm in depth (including the myometrial layer) on the LUS and two randomly selected samples of 5 mm depth from the IUO of the cervix. All histological samples were studied to quantify the distribution of A nerve fibers. The authors demonstrated a significant and notably higher concentration of A fibers in the IUO (46 ± 4.8) than in the LUS (21 ± 2.6), showing that the pregnant cervix has a greater concentration of A neurofibers than the at-term LUS. Pregnant women’s mechanosensitive pacemakers can operate normally when the body is in a physiological state, which permits normal uterine contractions and eutocic delivery. The increased frequency of C neurofibers in the cervix may influence the smooth muscle cell bundles’ activation, which could cause an aberrant mechano-sensitive pacemaker activation–deactivation cycle. Stressful circumstances (anxiety, tension, fetal head position) cause the sympathetic nervous system to become more active, working through these nerve fibers in the gravid cervix. They might interfere with the mechano-sensitive pacemakers, slowing down the uterine contractions and cervix ripening, which could result in dystocic labor.

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Section: Discussionmentioning

confidence: 99%

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Malvasi,

Baldini,

Cicinelli

et al. 2024

IJMS

Self Cite

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Integrative Analysis of Genomic Data Types and AI Methodologies in Healthcare Applications

Rahem,

Al-Akayleh

2024

2024 2nd International Conference on Cyber Resilience (ICCR)

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AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree

Malvasi,

Malgieri,

Cicinelli

et al. 2024

J. Imaging

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Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson’s correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson’s correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.

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Ontologies, Machine Learning and Deep Learning in Obstetrics

Cited by 3 publications

References 63 publications

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Integrative Analysis of Genomic Data Types and AI Methodologies in Healthcare Applications

AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree

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