{
  "abstract": "Introduction: 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.\n\nMethods: 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.\n\nResults: 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.\n\nDiscussion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ultrasound, Haidian District Maternal and Child Health Hospital , ,",
    "Department of Prenatal Diagnosis, Haidian District Maternal and Child Health Hospital , ,",
    "Senior Department of Orthopaedics, Fourth Medical Center of PLA General Hospital , ,",
    "Director of the Department of Child Health and the Division of Comprehensive Early Childhood Development, Haidian District Maternal and Child Health Hospital , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wenjing Chi"
    },
    {
      "affs": [
        1
      ],
      "name": "Hong Qi"
    },
    {
      "affs": [
        0
      ],
      "name": "Huawei Zhao"
    },
    {
      "affs": [
        2
      ],
      "name": "Hui Cheng"
    },
    {
      "affs": [
        3
      ],
      "name": "Quanlian Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuai Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/37cm0etb093n8.bib",
  "cited_by": 0,
  "doi": "10.3389/fped.2026.1939157",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719586/?tool=EBI",
  "id": "37cm0etb093n8",
  "issns": [
    "2296-2360"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719586",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7222564884",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719586/pdf/?tool=EBI",
  "pmcid": "PMC13719586",
  "pmid": "42964344",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/37cm0etb093n8.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719586",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719586"
    },
    {
      "first_seen": "2026-11-02T12:23:31Z",
      "native_id": "PMC13719586",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fped.2026.1939157"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Multihipposenet: multi-task deep learning for automated DDH assessment in infant hip ultrasound",
  "title_text": "Multihipposenet: multi-task deep learning for automated DDH assessment in infant hip ultrasound",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:37Z",
  "url": "https://research.draconitic.com/paper/37cm0etb093n8",
  "venue": "Frontiers in pediatrics",
  "versions": []
}