ArticleAbdominal radiology (New York)2026
Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis study presents GA-MTNet (Gestational-Age-Conditioned Multi-Task Network), a deep learning framework that integrates gestational age conditioning into a multi-task architecture to simultaneously perform abdominal landmark detection and classify five sonographic patterns linked to fetal growth conditions in prenatal ultrasound. MATERIALS AND
methodsA retrospective multi-center dataset of 9,046 B-mode ultrasound images from 2,712 pregnant women (18-34 weeks) was collected across three institutions. Data from Centers A and B (6,750 images, 2,278 patients) were used for model development via patient-stratified 5-fold cross-validation, while Center C (1,296 images, 434 patients) served as an external test set. GA-MTNet employs a Swin Transformer V1 backbone with a Gestational Age Embedding Module based on Feature-wise Linear Modulation (FiLM), enabling dynamic adaptation of image features to fetal developmental stage. Three task heads handle: (1) detection of four abdominal landmarks, (2) five-class classification of fetal growth conditions (IUGR, GDM, pre-eclampsia, normal growth, amniotic fluid abnormalities), and (3) biometric regression. Post-deployment adaptability is supported through uncertainty-guided continual learning combining Monte Carlo Dropout, Elastic Weight Consolidation, and Low-Rank Adaptation. Interpretability was assessed via SHAP and Grad-CAM++, scored by two blinded fetal medicine specialists.
resultsInternally, GA-MTNet achieved a macro-AUC of 0.863, accuracy of 84.7%, macro-F1 of 0.821, and landmark detection mAP@0.5 of 0.791. Externally, performance remained strong (AUC 0.841, accuracy 82.3%, F1 0.798), with only a 2.2-point AUC drop. Per-class AUC ranged from 0.803 to 0.921. Ablation studies confirmed significant contributions from gestational age conditioning (+ 3.1 pp, p = 0.003), multi-task learning (+ 1.0 pp, p = 0.011), and continual learning (+ 1.0 pp, p = 0.014). Calibration was excellent (ECE = 0.047), and clinical relevance scored 3.2/4.
conclusionsGA-MTNet demonstrates that gestational age conditioning meaningfully improves sonographic pattern recognition and landmark detection across the second and third trimesters, while its continual learning mechanism enables safe, practical post-deployment model refinement.
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