Authors
Wenjing Chi, Hong Qi, Huawei Zhao, Hui Cheng, Quanlian Yuan, Shuai Zheng
Published in
Frontiers in pediatrics. Volume 14. Pages 1939157. Epub Oct 16, 2026.
Abstract
Developmental dysplasia of the hip (DDH) is a common skeletal condition in infants and young children. For infants under six months, diagnosis relies on Graf ultrasonography to measure the α and β angles, which assess hip-joint development. However, this method requires experienced sonographers with expertise in complex anatomy.
In this study, we propose an end-to-end multi-task learning network that improves DDH assessment by jointly exploiting complementary anatomical information. First, we use ResNet-50 with a feature pyramid network (FPN) and a contextual semantic fusion module to extract multi-scale features from hip anatomy. Second, we employ a multi-expert gated channel transformation (ME-GCT) unit that explicitly models the relationships between tasks and channels to balance collaboration and conflict across tasks. Finally, a multi-task graph adaptive learning mechanism (MT-GALM) mines the hidden dynamic dependencies between adjacent channels across tasks. Two datasets comprising 1,355 annotated hip ultrasound images from 566 infants were constructed for this work.
Across the patient-level cross-validation test folds, 97.97% and 96.81% of the estimated α and β angles fall within 5 degrees of the expert reference.
Because validation was restricted to Graf type I and type II hips, these results should be interpreted as evidence of technical feasibility rather than of established clinical utility; evaluation on type III and type IV hips is required before clinical deployment.
PMID:
42964344
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.
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