Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

Purpose: The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants. Materials and methods: Of 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model. Results: The XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth. Conclusion: We have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.ope

White matter connectomes at birth accurately predict cognitive abilities at age 2

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study

Dataan perustuva tapa ennustaa vastasyntyneiden lääketieteellisiä diagnooseja

Preterm infants with a very low birth weight are at a great risk of dying or of developing certain life-threatening complications due to their underdevelopment. These critically ill infants are treated at neonatal intensive care units, in which their physiological condition is monitored continuously. In this thesis, machine learning is applied on the monitored parameter recordings and other patient-specific information from Children's Hospital, Helsinki University Hospital. The purpose is to use binary classifiers to predict neonatal mortality and occurrence of three morbidities: bronchopulmonary dysplasia, necrotising enterocolitis, and retinopathy of prematurity. Majority of the current studies have focused on comparing only a few classifiers. Therefore, a wider comparison of classifier algorithms is performed in this work. In addition to a common measure, the prediction performance is evaluated with two less used measures: F1 score and area under the precision-recall curve. Additionally, the impact of data preprocessing and feature selection on the prediction result is studied. The results show large differences in the performance of classifiers. Random forests, k-nearest neighbours, and logistic regression result in the highest F1 scores. The highest values of area under the precision-recall curve are achieved by random forests along with Gaussian processes. If area under the ROC curve is measured, random forests, Gaussian processes, and support vector machines perform the best. The monitored physiological parameters are time series and their sampling technique can be altered. This shows only a negligible impact on the results. However, lengthening the monitoring time of physiological parameters to 36-48 hours has a little but positive effect on the results. On the other hand, feature selection has a significant role: birth weight and gestational age are crucial for a high performance. Further, combining them with other features improves the performance. For all that, the optimal data preprocessing procedure is classifier- and complication-specific.Syntymäpainoltaan hyvin pienet keskoset ovat suuressa riskissä kuolla tai saada hengenvaarallisia komplikaatioita alikehittyneisyyden takia. Näitä vakavasti sairaita vauvoja hoidetaan vastasyntyneiden teho-osastoilla, joissa heidän fysiologista kuntoaan valvotaan jatkuvasti. Tämä tutkielma soveltaa koneoppimista valvottujen parametrien tallenteisiin ja muihin potilaskohtaisiin tietoihin, jotka on saatu HUS:n Lastenklinikalta. Tarkoituksena on käyttää binääristä luokittelua ennustamaan vastasyntyneiden kuolleisuutta ja kolmen sairauden puhkeamista. Nämä sairaudet ovat bronkopulmonaalinen dysplasia, nekrotisoiva enterokoliitti sekä keskosten retionopatia. Suurin osa nykyisestä tutkimuksesta on keskittynyt vertailemaan vain muutamia luokittelijoita. Tässä työssä vertaillaan siksi suurempaa määrää eri luokittelualgoritmeja. Yhden yleisesti käytetyn mitan lisäksi ennusteita arvioidaan myös kahdella vähemmän käytetyllä arviointimitalla: F1-arvolla ja tarkkuus-herkkyys-käyrän alapuolisella alueella. Myös datan esikäsittelyn ja piirteiden valinnan vaikutusta ennustustulokseen tutkitaan. Tulokset osoittavat suuria eroja eri luokittelijoiden välillä. Satunnaismetsillä, k-lähimmän naapurin luokittimella sekä logistisella regressiolla saadaan korkeimmat F1-arvot. Suurimmat tarkkuus-herkkyys-käyrän alapuoliset alueet saavutetaan satunnaismetsillä sekä Gaussisten prosessien luokittimilla. Jos taas ROC-käyrän alapuolinen alue mitataan, satunnaismetsät, Gaussisten prosessien luokitin ja tukivektorikoneet toimivat parhaiten. Seuratut fysiologiset parametrit ovat aikasarjoja, joten niiden näytteenottotapaa voidaan muuttaa. Tällä on vain pieni vaikutus tuloksiin. Fysiologisten parametrien seuranta-ajan pidentämisellä 36-48 tuntiin on kuitenkin pieni, mutta myönteinen vaikutus tuloksiin. Piirteiden valinnalla on puolestaan merkittävästi väliä: syntymäpaino ja gestaatioikä ovat ratkaisevia hyvien tulosten saamiseksi. Niiden yhdistäminen muiden piirteiden kanssa parantaa tuloksia. Ihanteellinen datan esikäsittely on kaikesta huolimatta luokittelija- ja komplikaatiokohtaista

Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data

IntroductionPreterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.MethodsThis study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR). A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models’ trustworthiness using calibration and cumulative curves. The visualized global interpretations using Shapley additive explanation (SHAP) is provided for analyzing variables’ contribution to final prediction.ResultsBased on the experimental results with area under the curve, the discrimination ability of the SVM algorithm was found to better than that of the LR model on three of the four datasets with 81%, 76% and 72% in weight follow-up, length baseline and length follow-up dataset respectively. The LR classifier achieved a better ROC score only on the weight baseline dataset with 83%. The global interpretability results revealed that pregnancy-induced hypertension, gestational age, twin birth, birth weight, antenatal corticosteroid use, premature rupture of membranes, sex, and birth length were consistently ranked as important variables in both the baseline and follow-up datasets.DiscussionThe application of machine learning models to the early detection and automated classification of short-term growth outcomes in preterm infants achieved high accuracy and may provide an efficient framework for clinical decision systems enabling more effective monitoring and facilitating timely intervention

Obesity in pregnancy: risk of gestational diabetes

Background: Maternal obesity is a risk factor for gestational diabetes and other adverse pregnancy outcomes, but the body fat distribution may be a more important risk factor than body mass index. Pregnancy is an insulin resistant state and more so, in obese women. Metformin could be beneficial in obese pregnant women due to its insulin sensitizing action. The aims of this study are to investigate visceral fat mass as a risk factor for gestational diabetes (VFM study), to develop a mathematical model for the prediction of gestational diabetes in obese women (VFM study) and to examine the effect of metformin on pregnancy outcomes in obese non-diabetic women (MOP Trial). Methods and Results: VFM study: The body composition of 302 obese pregnant women was assessed using bioelectrical impedance. A mathematical model to predict gestational diabetes using machine learning was developed using visceral fat mass which is a novel risk factor in addition to conventional risk factors. 72 of the women developed gestational diabetes (GDM). These women had higher visceral fat mass. Women with a baseline visceral fat mass ≥ 75th percentile, had a 3-fold risk of subsequent gestational diabetes. The mathematical model predicted gestational diabetes with an average overall accuracy of 77.5% and predicted birth centile classes with an average accuracy of 68%. According to the decision tree developed, VFM emerged as the most important variable in determining the risk of GDM and a VFM < 210 was used as the first split in the decision tree. MOP Trial: 133 obese pregnant women were randomised to either metformin or placebo. The pregnancy outcomes were compared in both groups. Insulin resistance was measured in all women. 118 women completed the trial. Metformin did not reduce the neonatal birth weight z-score, which was the primary outcome of the trial or the incidence of large for gestational age babies. However, metformin therapy significantly reduced gestational weight gain, reduced the pregnancy rise in visceral fat mass, and attenuated the expected physiological rise in insulin resistance at 28 weeks gestation. However, this did not result in an overall significant reduction in the incidence of gestational diabetes. There was a trend towards a reduced incidence of gestational diabetes in women with high baseline insulin resistance randomised to metformin. Conclusions: Visceral fat mass is a novel risk factor for gestational diabetes. The mathematical model successfully predicted gestational diabetes. Metformin reduced gestational weight gain and insulin resistance but did not lower the median neonatal birth weight or reduce the incidence of GDM

Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study

IntroductionThe aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort.MethodsWe included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013–2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable.ResultsWe included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants.ConclusionsWe developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field

The Association between Prenatal Maternal Mental Health and Infant Memory and Language Outcomes

Prenatal maternal depression and anxiety are associated with poor infant health, behavioral and achievement outcomes. The impacts of prenatal maternal mental health on the development of particular brain-based neurocognitive systems in children are less clear. This dissertation examines the association between prenatal maternal depression and anxiety and infant memory and language outcomes. 179 infant mother dyads were recruited in South Dakota. Ninety infants were followed at 9- and 15-months, and 89 were followed at 15- and 21-months of age. These data were used to understand more clearly the association between prenatal maternal depression and anxiety and changes in infant memory and language over the first two years of life. Additionally, by measuring the interaction between prenatal mental health and parenting and the direct association of parenting on changes in infant memory and language, we can better understand if the pathway between prenatal maternal mental health and infant memory and language is biological, social or both. Results demonstrated no significant direct association between prenatal maternal depression and anxiety symptoms and changes in infant memory or language from 9 to 21 months. The HOME language and literacy subscale was associated with changes in memory and language from 9 to 21 months; the HOME parental warmth subscale was associated with changes in language from 9 to 21 months. These results were independent of prenatal maternal social risk. Implications for additional screening measures, interventions, and considerations for future research are discussed

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants bornPeer reviewe

Predicting the earliest deviation in weight gain in the course towards manifest overweight in offspring exposed to obesity in pregnancy: a longitudinal cohort study

BACKGROUND: Obesity in pregnancy and related early-life factors place the offspring at the highest risk of being overweight. Despite convincing evidence on these associations, there is an unmet public health need to identify “high-risk” offspring by predicting very early deviations in weight gain patterns as a subclinical stage towards overweight. However, data and methods for individual risk prediction are lacking. We aimed to identify those infants exposed to obesity in pregnancy at ages 3 months, 1 year, and 2 years who likely will follow a higher-than-normal body mass index (BMI) growth trajectory towards manifest overweight by developing an early-risk quantification system. METHODS: This study uses data from the prospective mother-child cohort study Programming of Enhanced Adiposity Risk in CHildhood–Early Screening (PEACHES) comprising 1671 mothers with pre-conception obesity and without (controls) and their offspring. Exposures were pre- and postnatal risks documented in patient-held maternal and child health records. The main outcome was a “higher-than-normal BMI growth pattern” preceding overweight, defined as BMI z-score >1 SD (i.e., World Health Organization [WHO] cut-off “at risk of overweight”) at least twice during consecutive offspring growth periods between age 6 months and 5 years. The independent cohort PErinatal Prevention of Obesity (PEPO) comprising 11,730 mother-child pairs recruited close to school entry (around age 6 years) was available for data validation. Cluster analysis and sequential prediction modelling were performed. RESULTS: Data of 1557 PEACHES mother-child pairs and the validation cohort were analyzed comprising more than 50,000 offspring BMI measurements. More than 1-in-5 offspring exposed to obesity in pregnancy belonged to an upper BMI z-score cluster as a distinct pattern of BMI development (above the cut-off of 1 SD) from the first months of life onwards resulting in preschool overweight/obesity (age 5 years: odds ratio [OR] 16.13; 95% confidence interval [CI] 9.98–26.05). Contributing early-life factors including excessive weight gain (OR 2.08; 95% CI 1.25–3.45) and smoking (OR 1.94; 95% CI 1.27–2.95) in pregnancy were instrumental in predicting a “higher-than-normal BMI growth pattern” at age 3 months and re-evaluating the risk at ages 1 year and 2 years (area under the receiver operating characteristic [AUROC] 0.69–0.79, sensitivity 70.7–76.0%, specificity 64.7–78.1%). External validation of prediction models demonstrated adequate predictive performances. CONCLUSIONS: We devised a novel sequential strategy of individual prediction and re-evaluation of a higher-than-normal weight gain in “high-risk” infants well before developing overweight to guide decision-making. The strategy holds promise to elaborate interventions in an early preventive manner for integration in systems of well-child care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02318-z