Prediction models for intrauterine growth restriction using artificial intelligence and machine learning: a systematic review and meta-analysis
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Department of Translational Medicine (DiMeT), Università del Piemonte Orientale, Novara, Italy
Department of Translational Medicine (DiMeT), Università del Piemonte orientale, Novara, Italy
Publication date: 2023-04-27
Popul. Med. 2023;5(Supplement):A580
Intrauterine growth restriction (IUGR) is a fetal restriction associated with an abnormal fetal growth rate and has major implications for neonatal health. Artificial intelligence (AI) and machine learning (ML) models are increasingly 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 of AI/ML models in detecting fetuses at risk of IUGR. This SR was conducted according to the PRISMA checklist. SR included 14 studies reporting the performances of AI/ML models for the prediction of IUGR, of which 10 studies were used for meta-analysis. In the SR, the variables or data analyzed in the studies to predict IUGR were the fetal heart rate (n=7, 50%), biochemical or biological markers (n=4, 29%), DNA profiling data (n=2, 14%), and MRI data (n=1, 7%). 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 (0.80 - 0.88), specificity = 0.87 (95% CI 0.83 - 0.90), positive predictive value = 0.78 (0.68 - 0.86), negative predictive value = 0.91 (0.86 - 0.94) and diagnostic odds ratio = 30.97 (19.34 - 49.59). In detail, the RBF-SVM (Radial Basis Function - Support Vector Machine) model (with 93% accuracy) showed the best Results in predicting IUGR from FHR parameters derived from CTG.In 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.
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