Abstract
The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model's results using model-agnostic interpretable techniques, and to compare the results with clinical guidelines. This study used second-trimester ultrasound biometry and Doppler velocimetry were used to construct six FGR (birthweight < 3rd centile) ML models. Interpretability analysis was conducted using Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP). The results were compared with clinical guidelines based on the most optimal model. Support Vector Machine (SVM) exhibited the most consistent performance in FGR prediction. SHAP showed that the top contributors to identify FGR were Abdominal Circumference (AC), NF, Uterine RI (Ut RI), and Uterine PI (Ut PI). ALE showed that the cutoff values of Ut RI, Ut PI, and AC in differentiating FGR from normal were comparable with clinical guidelines (Errors between model and clinical; Ut RI: 15%, Ut PI: 8%, and AC: 11%). The cutoff value for NF to differentiate between healthy and FGR is 5.4 mm, where low NF may indicate FGR. The SVM model is the most stable in FGR prediction. ALE can be a potential tool to identify a cutoff value for novel parameters to differentiate between healthy and FGR.
Highlights
The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model’s results using modelagnostic interpretable techniques, and to compare the results with clinical guidelines
One interesting point that we observed was that central nervous system (CNS) related features—the Cisterna Magna (CM) and the Anterior Horn of Lateral Ventricle (Va), the Posterior Horn of Lateral Ventricle (Vp) hardly correlate with each other
NF measurements in the second trimester are often used to detect Down Syndromes[26–28], and there is a lack of study investigating the relationship between FGR and NF
Summary
The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model’s results using modelagnostic interpretable techniques, and to compare the results with clinical guidelines. This study aims to deepen the previous analysis work[11] using data inherited from previous work to provide an explanation of the model’s prediction using both global and local interpretability techniques. Such techniques can be useful for clinicians to understand the reasoning behind the model’s prediction. We carried out analysis by (i) investigating the effect of NF in predicting FGR using six ML models, (ii) explaining the ML results using model-agnostic interpretable techniques, and (iii) comparing the ’threshold’ obtained from the ML model to differentiate between normal and FGR with that of in clinical guidelines.
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