What are the novel findings of this work?Although suspected severe small-for-gestational age (SGA), defined as estimated fetal weight or abdominal circumference < 3 rd percentile or < 2 SD, is associated with a higher risk of perinatal complications, it performs poorly as a standalone parameter in predicting adverse perinatal outcome. The predictive performance of suspected severe SGA is similar to the individual performance of fetal cerebral Doppler and uterine artery Doppler parameters, as reported in the literature. The combination of these parameters may improve the overall prediction of abnormal perinatal outcome. What are the clinical implications of this work?Suspected severe SGA is not an accurate standalone predictor of adverse perinatal outcome. Given that severe SGA and abnormal Doppler parameters may reflect different pathological pathways, their combination could improve the overall prediction of abnormal perinatal outcome. A complete fetal examination incorporating fetal biometry and uterine artery and fetal Doppler evaluation should be performed when assessing for SGA.

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Diagnosing dementia, a syndrome that currently affects more than 55 million people worldwide, remains a particularly challenging and costly task. It may involve undertaking several medical tests such as brain scans, cognitive tests and genetic tests to determine the presence and degree of cognitive decline. These procedures are associated with long procedures, subjective evaluations and high costs. As a result, patients are often diagnosed at a late stage, when symptoms become highly pronounced. Therefore, there is an urgent need for developing new strategies for early, accurate and cost-effective dementia screening and risk prediction. To overcome current limitations, we explored readily available exposome predictors for identifying individuals at risk of dementia and compared traditional statistical modeling and advanced machine learning. From approximately 500,000 participants from the UK Biobank, 1523 participants diagnosed with dementia after their baseline assessment visit were included in our study. An equal number of healthy participants were randomly selected as the control group by matching statistical age mean and sex distribution. This resulted in a total of 3046 participants being selected for our study; 2740 participants from 19 of the 22 UK Biobank assessment centers were used for internal validation, and 306 participants from the remaining three centers were selected for external validation. We include data from the participants' baseline visit and selected 128 low-cost exposome factors related to life course exposures that may be easily acquired through simple questionnaires. Subsequently, data imputation was performed to account for missing patient data. Two different predictive models were assessed for discriminating between participants that remained healthy and participants diagnosed with dementia after the baseline visit, i.e. (1) a classical logistic regression linear classifier and (2) a machine learning ensemble classifier based on XGBoost. We interpreted the results by estimating feature importance within the predictive models. Our results demonstrate that machine learning models based on exposome data can reliably identify individuals that will be diagnosed with dementia. The XGBoost based model outperforms logistic regression model, achieving a mean AUC of 0.88 in the external validation tests. We identified novel exposome factors that might be combined as potential markers for dementia, such as facial aging, the frequency of use of sun/ultraviolet light protection, and the length of mobile phone use. Finally, we propose a novel neurocognitive assessment test that could be used as an online tool to screen individuals at risk of dementia for enrolment in preventive interventions and future clinical trials. Keywords: Machine learning, dementia, risk prediction, exposome predictors, low-cost prediction.