Early prediction of macrosomia based on an analysis of second trimester amniotic fluid by capillary electrophoresis

Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support

The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies.

A first trimester prediction model for large for gestational age infants: a preliminary study

Background

Large for gestational age infants (LGA) have increased risk of adverse short-term perinatal outcomes. This study aims to develop a multivariable prediction model for the risk of giving birth to a LGA baby, by using biochemical, biophysical, anamnestic, and clinical maternal characteristics available at first trimester.

Methods

Prospective study that included all singleton pregnancies attending the first trimester aneuploidy screening at the Obstetric Unit of the University Hospital of Modena, in Northern Italy, between June 2018 and December 2019.

Results

A total of 503 consecutive women were included in the analysis. The final prediction model for LGA, included multiparity (OR = 2.8, 95% CI: 1.6–4.9, p = 0.001), pre-pregnancy BMI (OR = 1.08, 95% CI: 1.03–1.14, p = 0.002) and PAPP-A MoM (OR = 1.43, 95% CI: 1.08–1.90, p = 0.013). The area under the ROC curve was 70.5%, indicating a satisfactory predictive accuracy. The best predictive cut-off for this score was equal to − 1.378, which corresponds to a 20.1% probability of having a LGA infant. By using such a cut-off, the risk of LGA can be predicted in our sample with sensitivity of 55.2% and specificity of 79.0%.

Conclusion

At first trimester, a model including multiparity, pre-pregnancy BMI and PAPP-A satisfactorily predicted the risk of giving birth to a LGA infant. This promising tool, once applied early in pregnancy, would identify women deserving targeted interventions.

Trial registration

ClinicalTrials.gov NCT04838431, 09/04/2021.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Objective

Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.

Materials and methods

We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.

Results

We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).

Conclusions

Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

Biomarkers for Macrosomia Prediction in Pregnancies Affected by Diabetes

Large birthweight, or macrosomia, is one of the commonest complications for pregnancies affected by diabetes. As macrosomia is associated with an increased risk of a number of adverse outcomes for both the mother and offspring, accurate antenatal prediction of fetal macrosomia could be beneficial in guiding appropriate models of care and interventions that may avoid or reduce these associated risks. However, current prediction strategies which include physical examination and ultrasound assessment, are imprecise. Biomarkers are proving useful in various specialties and may offer a new avenue for improved prediction of macrosomia. Prime biomarker candidates in pregnancies with diabetes include maternal glycaemic markers (glucose, 1,5-anhydroglucitol, glycosylated hemoglobin) and hormones proposed implicated in placental nutrient transfer (adiponectin and insulin-like growth factor-1). There is some support for an association of these biomarkers with birthweight and/or macrosomia, although current evidence in this emerging field is still limited. Thus, although biomarkers hold promise, further investigation is needed to elucidate the potential clinical utility of biomarkers for macrosomia prediction for pregnancies affected by diabetes.

Artificial Intelligence Methodologies and Their Application to Diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

The Applications of Genetic Algorithms in Medicine

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

Prediction of gestational diabetes mellitus based on an analysis of amniotic fluid by capillary electrophoresis

Aim: To detect gestational diabetes mellitus biomarkers in human amniotic fluid collected for age-related genetic testing using capillary electrophoresis and a sophisticated data analysis methodology. Materials & methods: Amniotic fluid samples were separated by capillary electrophoresis. Samples were classified using a genetic algorithm with Bayesian benefit function. The best model maximized the sensitivity and specificity and employed a leave-one-out cross-validation strategy. Results: Gestational diabetes mellitus (GDM; n = 14) was distinguished from non-GDM (n = 95) with 86% sensitivity and 99% specificity using two wavelets. These wavelets were located in the unresolved protein region and on the edge of the maternally derived albumin peak. Conclusion: GDM is a maternal pathology; however, it was shown that it alters the biochemical profile of amniotic fluid. Testing for GDM is normally carried out at 24–28 weeks, but changes can be detected at 15 weeks gestation, suggesting that GDM onset occurs early in gestation.