Using Machine Learning to Predict Complications in Pregnancy:A Systematic Review

Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health

Prediction of Concurrent Hypertensive Disorders in Pregnancy and Gestational Diabetes Mellitus Using Machine Learning Techniques

Gestational diabetes mellitus and hypertensive disorders in pregnancy are serious maternal health conditions with immediate and lifelong mother-child health consequences. These obstetric pathologies have been widely investigated, but mostly in silos, while studies focusing on their simultaneous occurrence rarely exist. This is especially the case in the machine learning domain. This retrospective study sought to investigate, construct, evaluate, compare, and isolate a supervised machine learning predictive model for the binary classification of co-occurring gestational diabetes mellitus and hypertensive disorders in pregnancy in a cohort of otherwise healthy pregnant women. To accomplish the stated aims, this study analyzed an extract (n=4624, n_features=38) of a labelled maternal perinatal dataset (n=9967, n_fields=79) collected by the PeriData.Net® database from a participating community hospital in Southeast Wisconsin between 2013 and 2018. The datasets were named, “WiseSample” and “WiseSubset” respectively in this study. Thirty-three models were constructed with the six supervised machine learning algorithms explored on the extracted dataset: logistic regression, random forest, decision tree, support vector machine, StackingClassifier, and KerasClassifier, which is a deep learning classification algorithm; all were evaluated using the StratifiedKfold cross-validation (k=10) method. The Synthetic Minority Oversampling Technique was applied to the training data to resolve the class imbalance that was noted in the sub-sample at the preprocessing phase. A wide range of evidence-based feature selection techniques were used to identify the best predictors of the comorbidity under investigation. Multiple model performance evaluation metrics that were employed to quantitatively evaluate and compare model performance quality include accuracy, F1, precision, recall, and the area under the receiver operating characteristic curve. Support Vector Machine objectively emerged as the most generalizable model for identifying the gravidae in WiseSubset who may develop concurrent gestational diabetes mellitus and hypertensive disorders in pregnancy, scoring 100.00% (mean) in recall. The model consisted of 9 predictors extracted by the recursive feature elimination with cross-validation with random forest. Finding from this study show that appropriate machine learning methods can reliably predict comorbid gestational diabetes and hypertensive disorders in pregnancy, using readily available routine prenatal attributes. Six of the nine most predictive factors of the comorbidity were also in the top 6 selections of at least one other feature selection method examined. The six predictors are healthy weight prepregnancy BMI, mother’s educational status, husband’s educational status, husband’s occupation in one year before the current pregnancy, mother’s blood group, and mother’s age range between 34 and 44 years. Insight from this analysis would support clinical decision making of obstetric experts when they are caring for 1.) nulliparous women, since they would have no obstetric history that could prompt their care providers for feto-maternal medical surveillance; and 2.) the experienced mothers with no obstetric history suggestive of any of the disease(s) under this study. Hence, among other benefits, the artificial-intelligence-backed tool designed in this research would likely improve maternal and child care quality outcomes

Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning

Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, most previous ML-based methods have only focused on predicting a single outcome, ignoring the potential correlations between different outcomes, and potentially leading to suboptimal results and overfitting issues. In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. We then propose an MTL framework to jointly predict multiple adverse neonatal outcomes. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches. Extensive experiments have been conducted using Electronic Health Records (EHRs) from 121 preterm neonates. Empirical results demonstrate the effectiveness of the MTL framework. Furthermore, the feature importance is analyzed for each neonatal outcome, providing insights into model interpretability

Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity

Objective To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound. Methods More than 13¿000 non-clinical images and 900 fetal lung images were used to develop a computerized method based on texture analysis and machine learning algorithms, trained to predict neonatal respiratory morbidity risk on fetal lung ultrasound images. The method, termed ‘quantitative ultrasound fetal lung maturity analysis’ (quantusFLM™), was then validated blindly in 144 neonates, delivered at 28¿+¿0 to 39¿+¿0¿weeks' gestation. Lung ultrasound images in DICOM format were obtained within 48¿h of delivery and the ability of the software to predict neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, was determined. Results Mean (SD) gestational age at delivery was 36¿+¿1 (3¿+¿3) weeks. Among the 144 neonates, there were 29 (20.1%) cases of neonatal respiratory morbidity. Quantitative texture analysis predicted neonatal respiratory morbidity with a sensitivity, specificity, positive predictive value and negative predictive value of 86.2%, 87.0%, 62.5% and 96.2%, respectively. Conclusions Quantitative ultrasound fetal lung maturity analysis predicted neonatal respiratory morbidity with an accuracy comparable to that of current tests using amniotic fluid.Peer ReviewedPostprint (published version

Doctor of Philosophy

dissertationIn its report To Err is Human, The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by conventional methods. The objective of this study was to examine novel KDD techniques used by other disciplines to create predictive models using healthcare data and validate the results through clinical domain expertise and performance measures. Patient records for the present study were extracted from the enterprise data warehouse (EDW) from Intermountain Healthcare. Patients with reported adverse events were identified from ICD9 codes. A clinical classification of the ICD9 codes was developed, and the clinical categories were analyzed for risk factors for adverse events including adverse drug events. Pharmacy data were categorized and used for detection of drugs administered in temporal sequence with antidote drugs. Data sampling and data boosting algorithms were used as signal amplification techniques. Decision trees, Naïve Bayes, Canonical Correlation Analysis, and Sequence Analysis were used as machine learning algorithms. iv Performance measures of the classification algorithms demonstrated statistically significant improvement after the transformation of the dataset through KDD techniques, data boosting and sampling. Domain expertise was applied to validate clinical significance of the results. KDD methodologies were applied successfully to a complex clinical dataset. The use of these methodologies was empirically proven effective in healthcare data through statistically significant measures and clinical validation. Although more research is required, we demonstrated the usefulness of KDD methodologies in knowledge extraction from complex clinical data

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: Favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study

PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction

Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies. We applied the Intravoxel Incoherent Motion Model and novel multi-compartment models for MRI fetal analysis, which exhibit potential to provide a multi-organ FGR assessment, overcoming the limitations of empirical indicators - such as abnormal artery Doppler findings - to evaluate placental dysfunction. The placenta and fetal liver presented key differentiators between FGR and normal controls (decreased perfusion, abnormal fetal blood motion and reduced fetal blood oxygenation. This may be associated with the preferential shunting of the fetal blood towards the fetal brain. These features were further explored to determine their role in assessing FGR severity, by employing simple machine learning models to predict FGR diagnosis (100\% accuracy in test data, n=5), GA at delivery, time from MRI scan to delivery, and baby weight. Moreover, we explored the use of deep learning to regress the latter three variables. Image texture analysis of the fetal organs demonstrated prominent textural variations in the placental perfusion fractions maps between the groups (p$<$0.0009), and spatial differences in the incoherent fetal capillary blood motion in the liver (p$<$0.009). This research serves as a proof-of-concept, investigating the effect of FGR on fetal organs

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized

Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities