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

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

Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior

Background: We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods: Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if $1 area was ,5th centile and as normal if all areas were .5th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm. Results: Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum. Conclusions: Fetal brain MRI textural patterns were associate

Maternal health : cost analysis of introducing the Umbiflow Velocity Doppler System at primary health level : a pilot study conducted at Kraaifontein Community Health Centre and Durbanville Day Clinic

Background: A South African report, Saving Babies 2010-2011, reports 32,178 still births in a 2 year period of January 2010 to December 2011 within the 94% of the total hospitals who provide data to a Perinatal Problem Identification programme (PPIP). In order to deal with perinatal mortality, specifically Intra-Uterine Growth there is needed to equip the primary health care (PHC) with technology for monitoring. An instrument called the Umbiflow Doppler ultrasound machine has been developed and there is need to test its economic impact in the PHC. Methods: A cross- sectional analytical study was conducted in the Tygerberg Eastern Health District of the Metro Region of Western Cape, South Africa at two primary health care (PHC) facilities, one secondary level hospital, and one tertiary hospital namely Kraaifontein Community Health Centre (CHC), Durbanville Day Clinic, Karl Bremmer District Hospital, and Tygerberg Hospital respectively. The aim of the research was to conduct a cost analysis in the introduction of an Umbiflow Doppler machine in the primary health care with the major goal being to reduce the number of perinatal deaths in the public health system. A societal perspective was adopted. The cost analysis study was carried out on the already approved sample size of 139 patients stemming from the Umbiflow Clinical study. The inclusion criteria for patient participation was poor SF growth and late bookers >28 weeks attending Kraaifontein Community Health Care Centre and Durbanville Clinic for antenatal services

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Brain development in fetal growth restriction: A volumetric approach using fetal MRI

Fetal growth restriction is the failure of a fetus to achieve its full growth potential, resulting in a neonate that is small for its gestational age. The aetiology of fetal growth restriction is varied and fetal growth restriction secondary to placental insufficiency is attributed to a failure of trophoblast invasion leading to under perfusion of the uteroplacental bed. In response to the adverse conditions in-utero, fetuses tend to compensate by increasing blood flow to the essential organs such as the brain, heart, and adrenals, at the expense of other organs (cerebral redistribution). As a consequence, growth tends to be asymmetric, with maintenance of the head growth velocity while the other growth parameters tail off; an effect which is also known as the ‘brain sparing effect’. Despite this apparent brain sparing effect, children who were growth restricted in utero are at increased risk of developmental delay and behavioural problems. 30 growth restricted and 48 normally grown fetuses were recruited into this study and were imaged using both conventional ultrasound with Doppler assessment, as well as fetal MRI with ssFSE sequences through the feto-placental unit and fetal brain. A dynamic approach was taken when imaging the fetal brain to compensate for the presence of fetal motion. MR imaging of the feto-placental unit detected significant differences in placental appearance, significantly smaller volumes of intra-abdominal and intra-thoracic organs, and significantly smaller regional brain growth among growth restricted fetuses. MR studies of the placenta in fetal growth restriction demonstrated a placental phenotype in growth restricted pregnancies that is characterised by smaller placental volumes, a significant increase in the placental volume affected by apparent pathology on MRI and a thickened, globular placenta. Although placental volume increased with gestation in both groups, the placental volume remained significantly smaller in the growth restricted fetuses (p = 0.003). There was also a significant correlation between the percentage of placental volume affected by abnormal heterogeneity and the severity of fetal growth restriction (r = 0.82, p < 0.001), and an increase in the maximal placental thickness to placental volume ratio above the 95th centile for gestational age was associated with fetal and early neonatal mortality (relative risk = 7, 95%CI = 2.96 – 16.55, p < 0.001) (figure 3.6) MR studies of fetal intra-thoracic and intra-abdominal volumes showed that although the volume of the intra-thoracic and intra-abdonimal organs (heart, lungs, thymus, liver and kidney) increased as gestation increased in both groups, the volumes of all three structures remained smaller in growth restricted fetuses (p < 0.01) (Figures 4.7 - 4.9) compared with normally grown fetuses. MR studies of the fetal brain demonstrated smaller intracranial volume, total brain volume and cerebellar volume in growth restricted fetuses. In addition, growth restricted fetuses with early onset fetal growth restriction demonstrated smaller vermis height and a corresponding increase in the tegmento-vermian angle. Growth restricted fetuses also demonstrated a disproportionate decrease in extra- and intra-cerebral fluid. This thesis showed evidence of changes in regional and global organ growth in growth restricted fetuses using high resolution fetal MRI. It is hoped that future imaging studies could offer useful insights into the origins and clinical significance of these findings and its consequences for later neurodevelopment

Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

Background: In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods: We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results: The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning

Relationship between metabolic and anthropometric maternal parameters and the fetal autonomic nervous system

Pre-pregnancy obesity, defined as a body mass index (BMI) greater than or equal to 30 kg/m2, can have adverse effects on the health of newborns and can also lead to metabolic, cardiovascular and neurological diseases in the offspring as they grow older. In the area of fetal origins and disease in adult life, a large number of studies have reported a critical role for maternal weight and metabolism before or during gestation in shaping the health of their offspring. Maternal obesity is recognised as a major modifiable contributor to obesity and metabolic syndrome in offspring, but the underlying factors remain unclear. The fetal autonomic nervous system (ANS) is subject to programming during developmental periods and is considered one of the processes by which early programming of disease can take place. The main goal of the present work was to use the fetal heart rate (HR) and heart rate variability (HRV) as proxies for the fetal ANS to study the effects of metabolic and anthropometric maternal (MAM) parameters before and during gestation on the fetuses of healthy, normoglycemic mothers. A total of 184 women in their second/third trimesters of uncomplicated pregnancies were included in this study. Pre-pregnancy BMI and maternal weight gain during pregnancy were recorded. In a subsample (n = 104), maternal insulin sensitivity was measured during an oral glucose tolerance test. Fetal HR and HRV were determined by magnetic recording in all subjects. The influence of pre-pregnancy BMI, maternal weight gain and maternal insulin sensitivity on fetal HR and HRV was evaluated. Associations between MAM parameters and maternal HR and HRV were also assessed. ANCOVA, partial correlation and mediation analysis were applied, all of which were adjusted for gestational age, gender and parity. A regression on fetal HR using a machine learning approach was tested to explore which maternal factor is the driving factor programming the fetal ANS. Four models were tested: Linear regression, Regression Tree, Support Vector Machine and Random Forest. The fetal HR was higher in fetuses of mothers with high pre-pregnancy BMI (overweight/obese) than in mothers with normal weight. The fetal HRV was lower in mothers with high weight gain than in mothers with normal weight gain. The fetal HR was negatively correlated with maternal weight gain and maternal insulin sensitivity. Pre-pregnancy BMI was positively correlated with fetal high frequency and negatively correlated with low frequency and the low to high frequency ratio. Maternal weight gain was associated indirectly with birth weight through fetal HR, while maternal insulin sensitivity was associated with fetal HR through fetal HRV. Separately, fetal HRV was associated with birth weight through the fetal HR. The Random Forest ensemble tree-based model outperformed linear regression as the fetal HR regression model. Fetal HR can be predicted using the following nine relevant variables (sorted from the most important to the least important): pre-pregnancy BMI, gender, maternal fasting insulin, maternal insulin sensitivity, gravidity, maternal age, maternal fasting glucose, gestational age and maternal weight gain. Pre-pregnancy BMI appeared to be the major factor predicting fetal HR. In conclusion, the fetal ANS is sensitive to maternal metabolic and anthropometric influences, and particularly maternal weight before pregnancy. These findings support the concept of the “Developmental Origin of Health and Disease” and increase our knowledge about the importance of the intrauterine environment in the programming of the ANS and the possible programming of disease in later life

Quantification of Placental Dysfunction in Pregnancy Complications

Background The pathogenetic mechanisms behind placental dysfunction-related complications like preeclampsia and intrauterine growth restriction have remained perplexing till now, in part because of lack of well-defined structural and functional molecular characterisation. There is growing evidence that links trophoblast debris and the existence of syncytial nuclear aggregates (SNA) to the pathogenesis of gestational diseases. Characterisation and quantification of structural and functional parameters of placental dysfunction may give researchers a clearer picture of the mechanisms underlying the development of high risk pregnancy. Methods Placental samples were obtained from normal term pregnancies, preterm controls, as well as from pregnancies complicated by preeclampsia (PET), intrauterine growth restriction (IUGR) and PET-IUGR. Formalin-fixed, paraffin-embedded sections were visualised with H&E, stained using immunohistochemistry (IHC) and digitally scanned. Using stereological methodology, volumes of placental SNAs, trophoblasts, villi and capillaries were measured. Three dimensional (3D) volume reconstructions of terminal placental villi with SNAs and fibrinoid degenerations were created. IHC-labelled slides were analysed by image analysis algorithms. Differential expression of placental genes and miRNAs, hypothesised to regulate cell death in placental dysfunction, were quantified using RT-qPCR. BeWo cell lines were carried out for in vitro validation of the effects miRNAs regulating programmed cell death (PCD) using flow cytometry and western blotting. Results Specific morphometric patterns of villous, trophoblasts, SNA and capillary volumes were demonstrated with characteristic higher SNAs and lower capillary volumes in PET placentae with reciprocal patterns in IUGR placentae showing a negative correlation pattern between nuclear aggregates and capillary volumes. Image analysis of immune-labelled slides showed a higher autophagy marker expression in PET and a positive correlation to SNAs as well as a balanced reciprocal expression patterns with apoptosis. Moreover, miR-204 transfected BeWo cells showed a similar balanced reciprocal regulation of autophagy and apoptosis expressions. Conclusion We have demonstrated that applying stereology-based and image analysis on digitised placental sections can be useful in quantifying and dissecting structural and functional patterns in normal and abnormal placental function. 3D reconstruction model are a novel approach towards placental characterisation in normal and complicated pregnancies. The study also showed that miR-204 plays a vital role in the regulation of placental autophagy and apoptosis, critical in the pathophysiology of placental dysfunction

Quantification of Placental Dysfunction in Pregnancy Complications

Background The pathogenetic mechanisms behind placental dysfunction-related complications like preeclampsia and intrauterine growth restriction have remained perplexing till now, in part because of lack of well-defined structural and functional molecular characterisation. There is growing evidence that links trophoblast debris and the existence of syncytial nuclear aggregates (SNA) to the pathogenesis of gestational diseases. Characterisation and quantification of structural and functional parameters of placental dysfunction may give researchers a clearer picture of the mechanisms underlying the development of high risk pregnancy. Methods Placental samples were obtained from normal term pregnancies, preterm controls, as well as from pregnancies complicated by preeclampsia (PET), intrauterine growth restriction (IUGR) and PET-IUGR. Formalin-fixed, paraffin-embedded sections were visualised with H&E, stained using immunohistochemistry (IHC) and digitally scanned. Using stereological methodology, volumes of placental SNAs, trophoblasts, villi and capillaries were measured. Three dimensional (3D) volume reconstructions of terminal placental villi with SNAs and fibrinoid degenerations were created. IHC-labelled slides were analysed by image analysis algorithms. Differential expression of placental genes and miRNAs, hypothesised to regulate cell death in placental dysfunction, were quantified using RT-qPCR. BeWo cell lines were carried out for in vitro validation of the effects miRNAs regulating programmed cell death (PCD) using flow cytometry and western blotting. Results Specific morphometric patterns of villous, trophoblasts, SNA and capillary volumes were demonstrated with characteristic higher SNAs and lower capillary volumes in PET placentae with reciprocal patterns in IUGR placentae showing a negative correlation pattern between nuclear aggregates and capillary volumes. Image analysis of immune-labelled slides showed a higher autophagy marker expression in PET and a positive correlation to SNAs as well as a balanced reciprocal expression patterns with apoptosis. Moreover, miR-204 transfected BeWo cells showed a similar balanced reciprocal regulation of autophagy and apoptosis expressions. Conclusion We have demonstrated that applying stereology-based and image analysis on digitised placental sections can be useful in quantifying and dissecting structural and functional patterns in normal and abnormal placental function. 3D reconstruction model are a novel approach towards placental characterisation in normal and complicated pregnancies. The study also showed that miR-204 plays a vital role in the regulation of placental autophagy and apoptosis, critical in the pathophysiology of placental dysfunction