Gestational diabetes mellitus (GDM) promotes fetal hyperinsulinemia, leading to fat accumulation in the fetal liver detectable via routine B-mode ultrasound. No published work has applied deep learning to classify fetal liver echotexture for automated GDM-related metabolic assessment. We present a comparative study of six convolutional neural network architectures — ResNet-18, ResNet-34, ResNet-50, EfficientNet-B0, EfficientNet-B4, and EfficientNet-B7 — for binary classification of fetal abdominal circumference above the 75th percentile (CA\,>\,p75) from liver-only ultrasound images. A patient-stratified cohort of 232 patients (110 GDM, 122 controls) provided 1,733 matched liver-only images (from a full dataset of 2,047), with class imbalance addressed via minority oversampling and weighted focal loss. Models were selected by fbeta on validation and evaluated on a held-out test set using AUC, sensitivity, specificity, and F$_1$ with bootstrap 95\% confidence intervals. EfficientNet-B0 achieved the highest AUC of 0.618 (95\% CI: 0.537--0.694). All models achieved very high true sensitivity for the elevated-CA class (0.93--1.00) but very low true specificity (0.00--0.13), indicating over-prediction of the positive class driven by the combined oversampling and weighted focal loss strategy. These results establish the first baseline for automated deep-learning screening of fetal hepatic echotexture in gestational diabetes and motivate larger multi-centre validation.