Abstract
In the presence of gestational diabetes mellitus (GDM), the fetus is exposed to a hyperinsulinemia environment. This environment can cause a wide range of metabolic and fetal cardiac structural alterations. Fetal myocardial hypertrophy predominantly affecting the interventricular septum possesses a morphology of disarray similar to hypertrophic cardiomyopathy, and may be present in some GDM neonates after birth. Myocardial thickness may increase in GDM fetuses independent of glycemic control status and fetal weight. Fetal echocardiography performed on fetuses with GDM helps in assessing cardiac structure and function, and to diagnose myocardial hypertrophy. There are few studies in the literature which have established evidence for morphologic variation associated with cardiac hypertrophy among fetuses of GDM mothers. In this study, fetal ultrasound images of normal, pregestational diabetes mellitus (preGDM) and GDM mothers were used to develop a computer aided diagnostic (CAD) tool. We proposed a new method called local preserving class separation (LPCS) framework to preserve the geometrical configuration of normal and preGDM/GDM subjects. The generated shearlet based texture features under LPCS framework showed promising results compared with deep learning algorithms. The proposed method achieved a maximum accuracy of 98.15% using a support vector machine (SVM) classifier. Hence, this paradigm can be helpful to physicians in detecting fetal myocardial hypertrophy in preGDM/GDM mothers.
Highlights
D IABETES in pregnancy is one of the common risk factors for adverse perinatal outcomes, with a prevalence of 7% to 11% in India and up to 17% in South India [1], [2]
Neonates born to diabetic mothers — either gestational diabetes mellitus (GDM) or pregestational diabetes mellitus, that is, with diabetes only during or pre-existing before pregnancy, respectively— are at risk of cardiovascular disease due to structural cardiac defects or impaired myocardial function
It is noted that support vector machine (SVM)+POLY3 achieved 16.4%, 7.39%, and 1.85% higher accuracy compared to POLY1, POLY2, and radial basis function (RBF) kernels, respectively
Summary
D IABETES in pregnancy is one of the common risk factors for adverse perinatal outcomes, with a prevalence of 7% to 11% in India and up to 17% in South India [1], [2]. Neonates born to diabetic mothers — either gestational diabetes mellitus (GDM) or pregestational diabetes mellitus (preGDM), that is, with diabetes only during or pre-existing before pregnancy, respectively— are at risk of cardiovascular disease due to structural cardiac defects or impaired myocardial function. The hyperinsulinemic state interferes with fetal metabolism, resulting in increased expression and affinity of insulin receptors, which leads to proliferation and hypertrophy of cardiac myocytes Cardiac hypertrophy in these fetuses exhibits myocardial derangement similar to hypertrophic cardiomyopathy [9]. Screening fetal cardiac structure and function among GDM and preGDM mothers could be helpful to identify subtle pathology early in gestation [14]. To the best of our knowledge, this is the first work to identify the fetal cardiac structure of gestational diabetes mellitus mother automatically using US images.
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